By using Python and its libraries, in this project, I will load in the data, check for cleanliness, and then trim and clean your dataset for analysis:
After wrangling the data I will analyze the dataset and get some conclusions about it.
The datasets I will use are the ones described below:
First, I will gather data from a Twitter Archive of the famous Twitter account @dog_rates (WeRateDogs), in which users leave comments rating their dogs adding a funny comment to the rating. It is noteworthy that usually, the ratings have a denominator of 10 with numerators greater than this number, for example: 12/10.
Then, we will collect the number of retweets and the number of favorites per tweet, since this information is missing in our first dataset. I will obtain this information using Tweepy to query Twitter's API.
The last dataset is contributed by Udacity, since the course instructor collected images from the WeRateDogs archive and classified them by breed through neural networks. Thus, I obtain a table with the predictions of each dog image, the tweet ID, the image URL and the image number that corresponds to the safest prediction.
# Import all the libraries that I will be using for this project
import pandas as pd
import numpy as np
import requests
import os
import re
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# Read the twitter-archive-enhanced.csv and import it into a DataFrame
pd.set_option('display.max_colwidth', -1) # This will set the no truncate for pandas as well as for dask.
df_twitter = pd.read_csv('twitter-archive-enhanced.csv')
df_twitter.head()
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | rating_numerator | rating_denominator | name | doggo | floofer | pupper | puppo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | NaN | NaN | 2017-08-01 16:23:56 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892420643555336193/photo/1 | 13 | 10 | Phineas | None | None | None | None |
| 1 | 892177421306343426 | NaN | NaN | 2017-08-01 00:17:27 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892177421306343426/photo/1 | 13 | 10 | Tilly | None | None | None | None |
| 2 | 891815181378084864 | NaN | NaN | 2017-07-31 00:18:03 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891815181378084864/photo/1 | 12 | 10 | Archie | None | None | None | None |
| 3 | 891689557279858688 | NaN | NaN | 2017-07-30 15:58:51 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891689557279858688/photo/1 | 13 | 10 | Darla | None | None | None | None |
| 4 | 891327558926688256 | NaN | NaN | 2017-07-29 16:00:24 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1 | 12 | 10 | Franklin | None | None | None | None |
I had some issues due to the current Covid-19 situation and it was not possible to get the approval for the Twitter's developper account as soon as I would have liked for this project, as it is taking them longer. Therefore I had tu use the data already provided by Udacity: (tweet_json.txt).
import tweepy
from tweepy import OAuthHandler
import json
from timeit import default_timer as timer
# Query Twitter API for each tweet in the Twitter archive and save JSON in a text file
# These are hidden to comply with Twitter's API terms and conditions
consumer_key = 'HIDEN'
consumer_secret = 'HIDEN'
access_token = 'HIDEN'
access_secret = 'HIDEN'
auth = OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_secret)
api = tweepy.API(auth, wait_on_rate_limit=True)
# NOTE TO STUDENT WITH MOBILE VERIFICATION ISSUES:
# df_1 is a DataFrame with the twitter_archive_enhanced.csv file. You may have to
# change line 17 to match the name of your DataFrame with twitter_archive_enhanced.csv
# NOTE TO REVIEWER: this student had mobile verification issues so the following
# Twitter API code was sent to this student from a Udacity instructor
# Tweet IDs for which to gather additional data via Twitter's API
tweet_ids = df_twitter.tweet_id.values
len(tweet_ids)
# Query Twitter's API for JSON data for each tweet ID in the Twitter archive
count = 0
fails_dict = {}
start = timer()
# Save each tweet's returned JSON as a new line in a .txt file
with open('tweet_json.txt', 'w') as outfile:
# This loop will likely take 20-30 minutes to run because of Twitter's rate limit
for tweet_id in tweet_ids:
count += 1
print(str(count) + ": " + str(tweet_id))
try:
tweet = api.get_status(tweet_id, tweet_mode='extended')
print("Success")
json.dump(tweet._json, outfile)
outfile.write('\n')
except tweepy.TweepError as e:
print("Fail")
fails_dict[tweet_id] = e
pass
end = timer()
print(end - start)
print(fails_dict)
1: 892420643555336193
Fail
2: 892177421306343426
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3: 891815181378084864
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
1980: 672980819271634944
Fail
1981: 672975131468300288
Fail
1982: 672970152493887488
Fail
1983: 672968025906282496
Fail
1984: 672964561327235073
Fail
1985: 672902681409806336
Fail
1986: 672898206762672129
Fail
1987: 672884426393653248
Fail
1988: 672877615439593473
Fail
1989: 672834301050937345
Fail
1990: 672828477930868736
Fail
1991: 672640509974827008
Fail
1992: 672622327801233409
Fail
1993: 672614745925664768
Fail
1994: 672609152938721280
Fail
1995: 672604026190569472
Fail
1996: 672594978741354496
Fail
1997: 672591762242805761
Fail
1998: 672591271085670400
Fail
1999: 672538107540070400
Fail
2000: 672523490734551040
Fail
2001: 672488522314567680
Fail
2002: 672482722825261057
Fail
2003: 672481316919734272
Fail
2004: 672475084225949696
Fail
2005: 672466075045466113
Fail
2006: 672272411274932228
Fail
2007: 672267570918129665
Fail
2008: 672264251789176834
Fail
2009: 672256522047614977
Fail
2010: 672254177670729728
Fail
2011: 672248013293752320
Fail
2012: 672245253877968896
Fail
2013: 672239279297454080
Fail
2014: 672231046314901505
Fail
2015: 672222792075620352
Fail
2016: 672205392827572224
Fail
2017: 672169685991993344
Fail
2018: 672160042234327040
Fail
2019: 672139350159835138
Fail
2020: 672125275208069120
Fail
2021: 672095186491711488
Fail
2022: 672082170312290304
Fail
2023: 672068090318987265
Fail
2024: 671896809300709376
Fail
2025: 671891728106971137
Fail
2026: 671882082306625538
Fail
2027: 671879137494245376
Fail
2028: 671874878652489728
Fail
2029: 671866342182637568
Fail
2030: 671855973984772097
Fail
2031: 671789708968640512
Fail
2032: 671768281401958400
Fail
2033: 671763349865160704
Fail
2034: 671744970634719232
Fail
2035: 671743150407421952
Fail
2036: 671735591348891648
Fail
2037: 671729906628341761
Fail
2038: 671561002136281088
Fail
2039: 671550332464455680
Fail
2040: 671547767500775424
Fail
2041: 671544874165002241
Fail
2042: 671542985629241344
Fail
2043: 671538301157904385
Fail
2044: 671536543010570240
Fail
2045: 671533943490011136
Fail
2046: 671528761649688577
Fail
2047: 671520732782923777
Fail
2048: 671518598289059840
Fail
2049: 671511350426865664
Fail
2050: 671504605491109889
Fail
2051: 671497587707535361
Fail
2052: 671488513339211776
Fail
2053: 671486386088865792
Fail
2054: 671485057807351808
Fail
2055: 671390180817915904
Fail
2056: 671362598324076544
Fail
2057: 671357843010908160
Fail
2058: 671355857343524864
Fail
2059: 671347597085433856
Fail
2060: 671186162933985280
Fail
2061: 671182547775299584
Fail
2062: 671166507850801152
Fail
2063: 671163268581498880
Fail
2064: 671159727754231808
Fail
2065: 671154572044468225
Fail
2066: 671151324042559489
Fail
2067: 671147085991960577
Fail
2068: 671141549288370177
Fail
2069: 671138694582165504
Fail
2070: 671134062904504320
Fail
2071: 671122204919246848
Fail
2072: 671115716440031232
Fail
2073: 671109016219725825
Fail
2074: 670995969505435648
Fail
2075: 670842764863651840
Fail
2076: 670840546554966016
Fail
2077: 670838202509447168
Fail
2078: 670833812859932673
Fail
2079: 670832455012716544
Fail
2080: 670826280409919488
Fail
2081: 670823764196741120
Fail
2082: 670822709593571328
Fail
2083: 670815497391357952
Fail
2084: 670811965569282048
Fail
2085: 670807719151067136
Fail
2086: 670804601705242624
Fail
2087: 670803562457407488
Fail
2088: 670797304698376195
Fail
2089: 670792680469889025
Fail
2090: 670789397210615808
Fail
2091: 670786190031921152
Fail
2092: 670783437142401025
Fail
2093: 670782429121134593
Fail
2094: 670780561024270336
Fail
2095: 670778058496974848
Fail
2096: 670764103623966721
Fail
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Fail
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Fail
2099: 670727704916926465
Fail
2100: 670717338665226240
Fail
2101: 670704688707301377
Fail
2102: 670691627984359425
Fail
2103: 670679630144274432
Fail
2104: 670676092097810432
Fail
2105: 670668383499735048
Fail
2106: 670474236058800128
Fail
2107: 670468609693655041
Fail
2108: 670465786746662913
Fail
2109: 670452855871037440
Fail
2110: 670449342516494336
Fail
2111: 670444955656130560
Fail
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Fail
2113: 670435821946826752
Fail
2114: 670434127938719744
Fail
2115: 670433248821026816
Fail
2116: 670428280563085312
Fail
2117: 670427002554466305
Fail
2118: 670421925039075328
Fail
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Fail
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Fail
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Fail
2122: 670408998013820928
Fail
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Fail
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Fail
2125: 670374371102445568
Fail
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Fail
2127: 670338931251150849
Fail
2128: 670319130621435904
Fail
2129: 670303360680108032
Fail
2130: 670290420111441920
Fail
2131: 670093938074779648
Fail
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Fail
2133: 670079681849372674
Fail
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Fail
2135: 670069087419133954
Fail
2136: 670061506722140161
Fail
2137: 670055038660800512
Fail
2138: 670046952931721218
Fail
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Fail
2140: 670037189829525505
Fail
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Fail
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Fail
2143: 669972011175813120
Fail
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Fail
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Fail
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Fail
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Fail
2148: 669753178989142016
Fail
2149: 669749430875258880
Fail
2150: 669684865554620416
Fail
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Fail
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Fail
2153: 669680153564442624
Fail
2154: 669661792646373376
Fail
2155: 669625907762618368
Fail
2156: 669603084620980224
Fail
2157: 669597912108789760
Fail
2158: 669583744538451968
Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
2165: 669371483794317312
Fail
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Fail
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Fail
2168: 669359674819481600
Fail
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Fail
2170: 669353438988365824
Fail
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Fail
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Fail
2173: 669327207240699904
Fail
2174: 669324657376567296
Fail
2175: 669216679721873412
Fail
2176: 669214165781868544
Fail
2177: 669203728096960512
Fail
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Fail
2179: 669015743032369152
Fail
2180: 669006782128353280
Fail
2181: 669000397445533696
Fail
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Fail
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Fail
2184: 668989615043424256
Fail
2185: 668988183816871936
Fail
2186: 668986018524233728
Fail
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Fail
2188: 668979806671884288
Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
2195: 668892474547511297
Fail
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Fail
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Fail
2198: 668826086256599040
Fail
2199: 668815180734689280
Fail
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Fail
2201: 668655139528511488
Fail
2202: 668645506898350081
Fail
2203: 668643542311546881
Fail
2204: 668641109086707712
Fail
2205: 668636665813057536
Fail
2206: 668633411083464705
Fail
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Fail
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Fail
2209: 668625577880875008
Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
2219: 668507509523615744
Fail
2220: 668496999348633600
Fail
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Fail
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Fail
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Fail
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Fail
2225: 668291999406125056
Fail
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Fail
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Fail
2228: 668268907921326080
Fail
2229: 668256321989451776
Fail
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Fail
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Fail
2232: 668226093875376128
Fail
2233: 668221241640230912
Fail
2234: 668204964695683073
Fail
2235: 668190681446379520
Fail
2236: 668171859951755264
Fail
2237: 668154635664932864
Fail
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Fail
2239: 668113020489474048
Fail
2240: 667937095915278337
Fail
2241: 667924896115245057
Fail
2242: 667915453470232577
Fail
2243: 667911425562669056
Fail
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Fail
2245: 667886921285246976
Fail
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Fail
2247: 667878741721415682
Fail
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Fail
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Fail
2250: 667861340749471744
Fail
2251: 667832474953625600
Fail
2252: 667806454573760512
Fail
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Fail
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Fail
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Fail
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Fail
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Fail
2258: 667728196545200128
Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
2267: 667530908589760512
Fail
2268: 667524857454854144
Fail
2269: 667517642048163840
Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
2277: 667443425659232256
Fail
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Fail
2279: 667435689202614272
Fail
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Fail
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Fail
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Fail
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Fail
2284: 667200525029539841
Fail
2285: 667192066997374976
Fail
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Fail
2287: 667182792070062081
Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
2311: 666786068205871104
Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
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Fail
2341: 666099513787052032
Fail
2342: 666094000022159362
Fail
2343: 666082916733198337
Fail
2344: 666073100786774016
Fail
2345: 666071193221509120
Fail
2346: 666063827256086533
Fail
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Fail
2348: 666057090499244032
Fail
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Fail
2350: 666051853826850816
Fail
2351: 666050758794694657
Fail
2352: 666049248165822465
Fail
2353: 666044226329800704
Fail
2354: 666033412701032449
Fail
2355: 666029285002620928
Fail
2356: 666020888022790149
Fail
373.00385841
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TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831670449226514432: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831650051525054464: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831552930092285952: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831322785565769729: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831315979191906304: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831309418084069378: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 831262627380748289: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 830956169170665475: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 830583320585068544: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 830173239259324417: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 830097400375152640: TweepError([{'code': 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TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690597161306841088: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690400367696297985: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690374419777196032: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690360449368465409: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690348396616552449: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690248561355657216: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690021994562220032: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690015576308211712: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 690005060500217858: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 689999384604450816: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 689993469801164801: TweepError([{'code': 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668988183816871936: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668986018524233728: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668981893510119424: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668979806671884288: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668975677807423489: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668967877119254528: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668960084974809088: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668955713004314625: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668932921458302977: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668902994700836864: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668892474547511297: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668872652652679168: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668852170888998912: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668826086256599040: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668815180734689280: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668779399630725120: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668655139528511488: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668645506898350081: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668643542311546881: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668641109086707712: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668636665813057536: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668633411083464705: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668631377374486528: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668627278264475648: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668625577880875008: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668623201287675904: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668620235289837568: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668614819948453888: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668587383441514497: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668567822092664832: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668544745690562560: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668542336805281792: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668537837512433665: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668528771708952576: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668507509523615744: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668496999348633600: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668484198282485761: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668480044826800133: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668466899341221888: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668297328638447616: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668291999406125056: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668286279830867968: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668274247790391296: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668268907921326080: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668256321989451776: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668248472370458624: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668237644992782336: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668226093875376128: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668221241640230912: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668204964695683073: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668190681446379520: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668171859951755264: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668154635664932864: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668142349051129856: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 668113020489474048: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667937095915278337: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667924896115245057: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667915453470232577: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667911425562669056: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667902449697558528: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667886921285246976: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667885044254572545: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667878741721415682: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667873844930215936: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667866724293877760: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667861340749471744: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667832474953625600: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667806454573760512: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667801013445750784: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667793409583771648: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667782464991965184: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667773195014021121: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667766675769573376: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667728196545200128: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667724302356258817: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667550904950915073: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667550882905632768: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667549055577362432: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667546741521195010: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667544320556335104: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667538891197542400: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667534815156183040: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667530908589760512: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667524857454854144: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667517642048163840: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667509364010450944: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667502640335572993: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667495797102141441: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667491009379606528: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667470559035432960: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667455448082227200: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667453023279554560: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667443425659232256: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667437278097252352: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667435689202614272: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667405339315146752: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667393430834667520: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667369227918143488: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667211855547486208: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667200525029539841: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667192066997374976: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667188689915760640: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667182792070062081: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667177989038297088: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667176164155375616: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667174963120574464: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667171260800061440: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667165590075940865: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667160273090932737: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667152164079423490: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667138269671505920: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667119796878725120: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667090893657276420: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667073648344346624: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667070482143944705: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667065535570550784: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667062181243039745: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667044094246576128: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 667012601033924608: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666996132027977728: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666983947667116034: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666837028449972224: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666835007768551424: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666826780179869698: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666817836334096384: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666804364988780544: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666786068205871104: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666781792255496192: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666776908487630848: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666739327293083650: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666701168228331520: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666691418707132416: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666649482315059201: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666644823164719104: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666454714377183233: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666447344410484738: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666437273139982337: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666435652385423360: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666430724426358785: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666428276349472768: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666421158376562688: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666418789513326592: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666411507551481857: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666407126856765440: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666396247373291520: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666373753744588802: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666362758909284353: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666353288456101888: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666345417576210432: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666337882303524864: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666293911632134144: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666287406224695296: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666273097616637952: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666268910803644416: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666104133288665088: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666102155909144576: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666099513787052032: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666094000022159362: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666082916733198337: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666073100786774016: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666071193221509120: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666063827256086533: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666058600524156928: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666057090499244032: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666055525042405380: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666051853826850816: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666050758794694657: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666049248165822465: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666044226329800704: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666033412701032449: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666029285002620928: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],), 666020888022790149: TweepError([{'code': 89, 'message': 'Invalid or expired token.'}],)}
# Read the JSON archive and create a DataFrame with the information needed
df_tw_json = open('tweet-json.txt', 'r')
df_twitter_counts = pd.DataFrame(columns=['tweet_id', 'retweets', 'favorites'])
for line in df_tw_json:
tweet = json.loads(line)
df_twitter_counts = df_twitter_counts.append({'tweet_id': tweet['id'],
'retweets': tweet['retweet_count'],
'favorites': tweet['favorite_count']},
ignore_index=True)
df_tw_json.close()
df_twitter_counts
| tweet_id | retweets | favorites | |
|---|---|---|---|
| 0 | 892420643555336193 | 8853 | 39467 |
| 1 | 892177421306343426 | 6514 | 33819 |
| 2 | 891815181378084864 | 4328 | 25461 |
| 3 | 891689557279858688 | 8964 | 42908 |
| 4 | 891327558926688256 | 9774 | 41048 |
| 5 | 891087950875897856 | 3261 | 20562 |
| 6 | 890971913173991426 | 2158 | 12041 |
| 7 | 890729181411237888 | 16716 | 56848 |
| 8 | 890609185150312448 | 4429 | 28226 |
| 9 | 890240255349198849 | 7711 | 32467 |
| 10 | 890006608113172480 | 7624 | 31166 |
| 11 | 889880896479866881 | 5156 | 28268 |
| 12 | 889665388333682689 | 8538 | 38818 |
| 13 | 889638837579907072 | 4735 | 27672 |
| 14 | 889531135344209921 | 2321 | 15359 |
| 15 | 889278841981685760 | 5637 | 25652 |
| 16 | 888917238123831296 | 4709 | 29611 |
| 17 | 888804989199671297 | 4559 | 26080 |
| 18 | 888554962724278272 | 3732 | 20290 |
| 19 | 888078434458587136 | 3653 | 22201 |
| 20 | 887705289381826560 | 5609 | 30779 |
| 21 | 887517139158093824 | 12082 | 46959 |
| 22 | 887473957103951883 | 18781 | 69871 |
| 23 | 887343217045368832 | 10737 | 34222 |
| 24 | 887101392804085760 | 6167 | 31061 |
| 25 | 886983233522544640 | 8084 | 35859 |
| 26 | 886736880519319552 | 3443 | 12306 |
| 27 | 886680336477933568 | 4610 | 22798 |
| 28 | 886366144734445568 | 3316 | 21524 |
| 29 | 886267009285017600 | 4 | 117 |
| ... | ... | ... | ... |
| 2324 | 666411507551481857 | 339 | 459 |
| 2325 | 666407126856765440 | 44 | 113 |
| 2326 | 666396247373291520 | 92 | 172 |
| 2327 | 666373753744588802 | 100 | 194 |
| 2328 | 666362758909284353 | 595 | 804 |
| 2329 | 666353288456101888 | 77 | 229 |
| 2330 | 666345417576210432 | 146 | 307 |
| 2331 | 666337882303524864 | 96 | 204 |
| 2332 | 666293911632134144 | 368 | 522 |
| 2333 | 666287406224695296 | 71 | 152 |
| 2334 | 666273097616637952 | 82 | 184 |
| 2335 | 666268910803644416 | 37 | 108 |
| 2336 | 666104133288665088 | 6871 | 14765 |
| 2337 | 666102155909144576 | 16 | 81 |
| 2338 | 666099513787052032 | 73 | 164 |
| 2339 | 666094000022159362 | 79 | 169 |
| 2340 | 666082916733198337 | 47 | 121 |
| 2341 | 666073100786774016 | 174 | 335 |
| 2342 | 666071193221509120 | 67 | 154 |
| 2343 | 666063827256086533 | 232 | 496 |
| 2344 | 666058600524156928 | 61 | 115 |
| 2345 | 666057090499244032 | 146 | 304 |
| 2346 | 666055525042405380 | 261 | 448 |
| 2347 | 666051853826850816 | 879 | 1253 |
| 2348 | 666050758794694657 | 60 | 136 |
| 2349 | 666049248165822465 | 41 | 111 |
| 2350 | 666044226329800704 | 147 | 311 |
| 2351 | 666033412701032449 | 47 | 128 |
| 2352 | 666029285002620928 | 48 | 132 |
| 2353 | 666020888022790149 | 532 | 2535 |
2354 rows × 3 columns
# Download programatically the image prediction file using the link provided by Udacity
url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'
r = requests.get(url, allow_redirects=True)
with open('image-predictions.tsv', mode ='wb') as file:
file.write(r.content)
# Read and display the data from the image predictions file
df_img_pred = pd.read_csv('image-predictions.tsv', sep = '\t')
df_img_pred.head()
| tweet_id | jpg_url | img_num | p1 | p1_conf | p1_dog | p2 | p2_conf | p2_dog | p3 | p3_conf | p3_dog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 666020888022790149 | https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg | 1 | Welsh_springer_spaniel | 0.465074 | True | collie | 0.156665 | True | Shetland_sheepdog | 0.061428 | True |
| 1 | 666029285002620928 | https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg | 1 | redbone | 0.506826 | True | miniature_pinscher | 0.074192 | True | Rhodesian_ridgeback | 0.072010 | True |
| 2 | 666033412701032449 | https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg | 1 | German_shepherd | 0.596461 | True | malinois | 0.138584 | True | bloodhound | 0.116197 | True |
| 3 | 666044226329800704 | https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg | 1 | Rhodesian_ridgeback | 0.408143 | True | redbone | 0.360687 | True | miniature_pinscher | 0.222752 | True |
| 4 | 666049248165822465 | https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg | 1 | miniature_pinscher | 0.560311 | True | Rottweiler | 0.243682 | True | Doberman | 0.154629 | True |
First of all I will display the original datasets' first rows and get their information to have an idea of what I will be wrangling.
df_twitter.head()
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | rating_numerator | rating_denominator | name | doggo | floofer | pupper | puppo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | NaN | NaN | 2017-08-01 16:23:56 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892420643555336193/photo/1 | 13 | 10 | Phineas | None | None | None | None |
| 1 | 892177421306343426 | NaN | NaN | 2017-08-01 00:17:27 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892177421306343426/photo/1 | 13 | 10 | Tilly | None | None | None | None |
| 2 | 891815181378084864 | NaN | NaN | 2017-07-31 00:18:03 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891815181378084864/photo/1 | 12 | 10 | Archie | None | None | None | None |
| 3 | 891689557279858688 | NaN | NaN | 2017-07-30 15:58:51 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891689557279858688/photo/1 | 13 | 10 | Darla | None | None | None | None |
| 4 | 891327558926688256 | NaN | NaN | 2017-07-29 16:00:24 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1 | 12 | 10 | Franklin | None | None | None | None |
df_twitter_counts.head()
| tweet_id | retweets | favorites | |
|---|---|---|---|
| 0 | 892420643555336193 | 8853 | 39467 |
| 1 | 892177421306343426 | 6514 | 33819 |
| 2 | 891815181378084864 | 4328 | 25461 |
| 3 | 891689557279858688 | 8964 | 42908 |
| 4 | 891327558926688256 | 9774 | 41048 |
df_img_pred.head()
| tweet_id | jpg_url | img_num | p1 | p1_conf | p1_dog | p2 | p2_conf | p2_dog | p3 | p3_conf | p3_dog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 666020888022790149 | https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg | 1 | Welsh_springer_spaniel | 0.465074 | True | collie | 0.156665 | True | Shetland_sheepdog | 0.061428 | True |
| 1 | 666029285002620928 | https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg | 1 | redbone | 0.506826 | True | miniature_pinscher | 0.074192 | True | Rhodesian_ridgeback | 0.072010 | True |
| 2 | 666033412701032449 | https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg | 1 | German_shepherd | 0.596461 | True | malinois | 0.138584 | True | bloodhound | 0.116197 | True |
| 3 | 666044226329800704 | https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg | 1 | Rhodesian_ridgeback | 0.408143 | True | redbone | 0.360687 | True | miniature_pinscher | 0.222752 | True |
| 4 | 666049248165822465 | https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg | 1 | miniature_pinscher | 0.560311 | True | Rottweiler | 0.243682 | True | Doberman | 0.154629 | True |
# Get information about the columns entries and theur datatype
df_twitter.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2356 entries, 0 to 2355 Data columns (total 17 columns): tweet_id 2356 non-null int64 in_reply_to_status_id 78 non-null float64 in_reply_to_user_id 78 non-null float64 timestamp 2356 non-null object source 2356 non-null object text 2356 non-null object retweeted_status_id 181 non-null float64 retweeted_status_user_id 181 non-null float64 retweeted_status_timestamp 181 non-null object expanded_urls 2297 non-null object rating_numerator 2356 non-null int64 rating_denominator 2356 non-null int64 name 2356 non-null object doggo 2356 non-null object floofer 2356 non-null object pupper 2356 non-null object puppo 2356 non-null object dtypes: float64(4), int64(3), object(10) memory usage: 313.0+ KB
The WeRateDogs Twitter archive contains basic tweet data for all 5000+ of their tweets, but not everything. One column the archive does contain though: each tweet's text, which I used to extract rating, dog name, and dog "stage" (i.e. doggo, floofer, pupper, and puppo) to make this Twitter archive "enhanced." Of the 5000+ tweets, I have filtered for tweets with ratings only (there are 2356).
df_twitter (Twitter archive) columns:
df_twitter_counts.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2354 entries, 0 to 2353 Data columns (total 3 columns): tweet_id 2354 non-null object retweets 2354 non-null object favorites 2354 non-null object dtypes: object(3) memory usage: 55.2+ KB
df_img_pred.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2075 entries, 0 to 2074 Data columns (total 12 columns): tweet_id 2075 non-null int64 jpg_url 2075 non-null object img_num 2075 non-null int64 p1 2075 non-null object p1_conf 2075 non-null float64 p1_dog 2075 non-null bool p2 2075 non-null object p2_conf 2075 non-null float64 p2_dog 2075 non-null bool p3 2075 non-null object p3_conf 2075 non-null float64 p3_dog 2075 non-null bool dtypes: bool(3), float64(3), int64(2), object(4) memory usage: 152.1+ KB
df_ing_pred is a table full of image predictions (the top three only) alongside each tweet ID, image URL, and the image number that corresponded to the most confident prediction (numbered 1 to 4 since tweets can have up to four images)
# Check for nulls
df_twitter.isnull().sum()
tweet_id 0 in_reply_to_status_id 2278 in_reply_to_user_id 2278 timestamp 0 source 0 text 0 retweeted_status_id 2175 retweeted_status_user_id 2175 retweeted_status_timestamp 2175 expanded_urls 59 rating_numerator 0 rating_denominator 0 name 0 doggo 0 floofer 0 pupper 0 puppo 0 dtype: int64
df_twitter_counts.isnull().sum()
tweet_id 0 retweets 0 favorites 0 dtype: int64
df_img_pred.isnull().sum()
tweet_id 0 jpg_url 0 img_num 0 p1 0 p1_conf 0 p1_dog 0 p2 0 p2_conf 0 p2_dog 0 p3 0 p3_conf 0 p3_dog 0 dtype: int64
# Get some statistical insight of the dataset
df_twitter.describe()
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | retweeted_status_id | retweeted_status_user_id | rating_numerator | rating_denominator | |
|---|---|---|---|---|---|---|---|
| count | 2.356000e+03 | 7.800000e+01 | 7.800000e+01 | 1.810000e+02 | 1.810000e+02 | 2356.000000 | 2356.000000 |
| mean | 7.427716e+17 | 7.455079e+17 | 2.014171e+16 | 7.720400e+17 | 1.241698e+16 | 13.126486 | 10.455433 |
| std | 6.856705e+16 | 7.582492e+16 | 1.252797e+17 | 6.236928e+16 | 9.599254e+16 | 45.876648 | 6.745237 |
| min | 6.660209e+17 | 6.658147e+17 | 1.185634e+07 | 6.661041e+17 | 7.832140e+05 | 0.000000 | 0.000000 |
| 25% | 6.783989e+17 | 6.757419e+17 | 3.086374e+08 | 7.186315e+17 | 4.196984e+09 | 10.000000 | 10.000000 |
| 50% | 7.196279e+17 | 7.038708e+17 | 4.196984e+09 | 7.804657e+17 | 4.196984e+09 | 11.000000 | 10.000000 |
| 75% | 7.993373e+17 | 8.257804e+17 | 4.196984e+09 | 8.203146e+17 | 4.196984e+09 | 12.000000 | 10.000000 |
| max | 8.924206e+17 | 8.862664e+17 | 8.405479e+17 | 8.874740e+17 | 7.874618e+17 | 1776.000000 | 170.000000 |
Rating numerator:
Rating denominator:
# How many retweets
sum(~df_twitter.retweeted_status_id.isnull())
181
# How many replies
sum(~df_twitter.in_reply_to_status_id.isnull())
78
# Check for duplicated twitter ids within the same dataframe
sum(df_twitter.tweet_id.duplicated())
0
sum(df_img_pred.tweet_id.duplicated())
0
sum(df_twitter_counts.tweet_id.duplicated())
0
# Check for unique values
df_twitter.nunique()
tweet_id 2356 in_reply_to_status_id 77 in_reply_to_user_id 31 timestamp 2356 source 4 text 2356 retweeted_status_id 181 retweeted_status_user_id 25 retweeted_status_timestamp 181 expanded_urls 2218 rating_numerator 40 rating_denominator 18 name 957 doggo 2 floofer 2 pupper 2 puppo 2 dtype: int64
df_img_pred.nunique()
tweet_id 2075 jpg_url 2009 img_num 4 p1 378 p1_conf 2006 p1_dog 2 p2 405 p2_conf 2004 p2_dog 2 p3 408 p3_conf 2006 p3_dog 2 dtype: int64
df_twitter_counts.nunique()
tweet_id 2354 retweets 1724 favorites 2007 dtype: int64
# Check for the different dog names in the twitter dataframe
df_twitter.name.value_counts()
None 745
a 55
Charlie 12
Oliver 11
Lucy 11
Cooper 11
Lola 10
Penny 10
Tucker 10
Winston 9
Bo 9
Sadie 8
the 8
Daisy 7
an 7
Toby 7
Buddy 7
Bailey 7
Koda 6
Jack 6
Leo 6
Dave 6
Bella 6
Jax 6
Scout 6
Milo 6
Oscar 6
Stanley 6
Rusty 6
George 5
..
Jay 1
Brockly 1
Olaf 1
Marty 1
Darrel 1
Kevon 1
Kloey 1
Robin 1
Blanket 1
O 1
Tuck 1
Jo 1
Iggy 1
Carper 1
Liam 1
Bode 1
Dallas 1
Zooey 1
Brady 1
Shikha 1
Banditt 1
Stefan 1
Lillie 1
Tom 1
Tove 1
Winifred 1
Simba 1
Craig 1
Goliath 1
Livvie 1
Name: name, Length: 957, dtype: int64
As we can already see there are some invalid names such as "None", "a", "an" or "By",let's take a deepest investigation on the name column and sort them by its first letter:
# Chech how many names are marked as "None"
len(df_twitter[df_twitter['name'] == "None"])
745
# Chech how many names are marked as "None"
df_twitter[(df_twitter['name']=='None')].text
5 Here we have a majestic great white breaching off South Africa's coast. Absolutely h*ckin breathtaking. 13/10 (IG: tucker_marlo) #BarkWeek https://t.co/kQ04fDDRmh
7 When you watch your owner call another dog a good boy but then they turn back to you and say you're a great boy. 13/10 https://t.co/v0nONBcwxq
12 Here's a puppo that seems to be on the fence about something haha no but seriously someone help her. 13/10 https://t.co/BxvuXk0UCm
24 You may not have known you needed to see this today. 13/10 please enjoy (IG: emmylouroo) https://t.co/WZqNqygEyV
25 This... is a Jubilant Antarctic House Bear. We only rate dogs. Please only send dogs. Thank you... 12/10 would suffocate in floof https://t.co/4Ad1jzJSdp
30 @NonWhiteHat @MayhewMayhem omg hello tanner you are a scary good boy 12/10 would pet with extreme caution
32 RT @Athletics: 12/10 #BATP https://t.co/WxwJmvjfxo
35 I have a new hero and his name is Howard. 14/10 https://t.co/gzLHboL7Sk
37 Here we have a corgi undercover as a malamute. Pawbably doing important investigative work. Zero control over tongue happenings. 13/10 https://t.co/44ItaMubBf
41 I present to you, Pup in Hat. Pup in Hat is great for all occasions. Extremely versatile. Compact as h*ck. 14/10 (IG: itselizabethgales) https://t.co/vvBOcC2VdC
42 OMG HE DIDN'T MEAN TO HE WAS JUST TRYING A LITTLE BARKOUR HE'S SUPER SORRY 13/10 WOULD FORGIVE IMMEDIATE https://t.co/uF3pQ8Wubj
47 Please only send dogs. We don't rate mechanics, no matter how h*ckin good. Thank you... 13/10 would sneak a pat https://t.co/Se5fZ9wp5E
55 @roushfenway These are good dogs but 17/10 is an emotional impulse rating. More like 13/10s
59 Ugh not again. We only rate dogs. Please don't send in well-dressed floppy-tongued street penguins. Dogs only please. Thank you... 12/10 https://t.co/WiAMbTkDPf
62 Please don't send in photos without dogs in them. We're not @porch_rates. Insubordinate and churlish. Pretty good porch tho 11/10 https://t.co/HauE8M3Bu4
64 @RealKentMurphy 14/10 confirmed
72 Martha is stunning how h*ckin dare you. 13/10 https://t.co/9uABQXgjwa
78 RT @rachel2195: @dog_rates the boyfriend and his soaking wet pupper h*cking love his new hat 14/10 https://t.co/dJx4Gzc50G
83 I can say with the pupmost confidence that the doggos who assisted with this search are heroic as h*ck. 14/10 for all https://t.co/8yoc1CNTsu
88 You'll get your package when that precious man is done appreciating the pups. 13/10 for everyone https://t.co/PFp4MghzBW
89 Guys please stop sending pictures without any dogs in th- oh never mind hello excuse me sir. 12/10 stealthy as h*ck https://t.co/brCQoqc8AW
93 I can't believe this keeps happening. This, is a birb taking a bath. We only rate dogs. Please only send dogs. Thank you... 12/10 https://t.co/pwY9PQhtP2
96 We usually don't rate Deck-bound Saskatoon Black Bears, but this one is h*ckin flawless. Sneaky tongue slip too. 13/10 would hug firmly https://t.co/mNuMH9400n
99 Here's a very large dog. He has a date later. Politely asked this water person to check if his breath is bad. 12/10 good to go doggo https://t.co/EMYIdoblMR
100 Here are my favorite #dogsatpollingstations \nMost voted for a more consistent walking schedule and to increase daily pats tenfold. All 13/10 https://t.co/17FVMl4VZ5
101 RT @loganamnosis: Penelope here is doing me quite a divertir. Well done, @dog_rates! Loving the pupdate. 14/10, je jouerais de nouveau. htt…
103 We. Only. Rate. Dogs. Do not send in other things like this fluffy floor shark clearly ready to attack. Get it together guys... 12/10 https://t.co/BZHiKx3FpQ
110 Never doubt a doggo 14/10 https://t.co/AbBLh2FZCH
112 Real funny guys. Sending in a pic without a dog in it. Hilarious. We'll rate the rug tho because it's giving off a very good vibe. 11/10 https://t.co/GCD1JccCyi
113 @ComplicitOwl @ShopWeRateDogs >10/10 is reserved for dogs
...
2294 Extremely intelligent dog here. Has learned to walk like human. Even has his own dog. Very impressive 10/10 https://t.co/0DvHAMdA4V
2298 After much debate this dog is being upgraded to 10/10. I repeat 10/10
2299 Here we have a Hufflepuff. Loves vest. Eyes wide af. Flaccid tail. Matches carpet. Always a little blurry. 8/10 https://t.co/7JdgVqDnvR
2301 12/10 gimme now https://t.co/QZAnwgnOMB
2305 My goodness. Very rare dog here. Large. Tail dangerous. Kinda fat. Only eats leaves. Doesn't come when called 3/10 https://t.co/xYGdBrMS9h
2306 These are Peruvian Feldspars. Their names are Cupit and Prencer. Both resemble Rand Paul. Sick outfits 10/10 & 10/10 https://t.co/ZnEMHBsAs1
2307 12/10 simply brilliant pup https://t.co/V6ZzG45zzG
2310 Unfamiliar with this breed. Ears pointy af. Won't let go of seashell. Won't eat kibble. Not very fast. Bad dog 2/10 https://t.co/EIn5kElY1S
2316 Cool dog. Enjoys couch. Low monotone bark. Very nice kicks. Pisses milk (must be rare). Can't go down stairs. 4/10 https://t.co/vXMKrJC81s
2320 Here we see a lone northeastern Cumberbatch. Half ladybug. Only builds with bricks. Very confident with body. 7/10 https://t.co/7LtjBS0GPK
2321 "Can you behave? You're ruining my wedding day"\nDOG: idgaf this flashlight tastes good as hell\n\n10/10 https://t.co/GlFZPzqcEU
2322 Oh boy what a pup! Sunglasses take this one to the next level. Weirdly folds front legs. Pretty big. 6/10 https://t.co/yECbFrSArM
2323 Here we have an Austrian Pulitzer. Collectors edition. Levitates (?). 7/10 would garden with https://t.co/NMQq6HIglK
2324 *internally screaming* 12/10 https://t.co/YMcrXC2Y6R
2328 Oh goodness. A super rare northeast Qdoba kangaroo mix. Massive feet. No pouch (disappointing). Seems alert. 9/10 https://t.co/Dc7b0E8qFE
2329 Those are sunglasses and a jean jacket. 11/10 dog cool af https://t.co/uHXrPkUEyl
2330 Unique dog here. Very small. Lives in container of Frosted Flakes (?). Short legs. Must be rare 6/10 would still pet https://t.co/XMD9CwjEnM
2331 Here we have a mixed Asiago from the Galápagos Islands. Only one ear working. Big fan of marijuana carpet. 8/10 https://t.co/tltQ5w9aUO
2332 Look at this jokester thinking seat belt laws don't apply to him. Great tongue tho 10/10 https://t.co/VFKG1vxGjB
2336 Can take selfies 11/10 https://t.co/ws2AMaNwPW
2337 Very concerned about fellow dog trapped in computer. 10/10 https://t.co/0yxApIikpk
2338 Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX
2339 Oh my. Here you are seeing an Adobe Setter giving birth to twins!!! The world is an amazing place. 11/10 https://t.co/11LvqN4WLq
2340 Can stand on stump for what seems like a while. Built that birdhouse? Impressive. Made friends with a squirrel. 8/10 https://t.co/Ri4nMTLq5C
2341 This appears to be a Mongolian Presbyterian mix. Very tired. Tongue slip confirmed. 9/10 would lie down with https://t.co/mnioXo3IfP
2342 Here we have a well-established sunblockerspaniel. Lost his other flip-flop. 6/10 not very waterproof https://t.co/3RU6x0vHB7
2343 Let's hope this flight isn't Malaysian (lol). What a dog! Almost completely camouflaged. 10/10 I trust this pilot https://t.co/Yk6GHE9tOY
2344 Here we have a northern speckled Rhododendron. Much sass. Gives 0 fucks. Good tongue. 9/10 would caress sensually https://t.co/ZoL8kq2XFx
2351 Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq
2355 Here we have a Japanese Irish Setter. Lost eye in Vietnam (?). Big fan of relaxing on stair. 8/10 would pet https://t.co/BLDqew2Ijj
Name: text, Length: 745, dtype: object
np.sort(df_twitter.name.unique())
array(['Abby', 'Ace', 'Acro', 'Adele', 'Aiden', 'Aja', 'Akumi', 'Al',
'Albert', 'Albus', 'Aldrick', 'Alejandro', 'Alexander',
'Alexanderson', 'Alf', 'Alfie', 'Alfy', 'Alice', 'Amber', 'Ambrose',
'Amy', 'Amélie', 'Anakin', 'Andru', 'Andy', 'Angel', 'Anna',
'Anthony', 'Antony', 'Apollo', 'Aqua', 'Archie', 'Arlen', 'Arlo',
'Arnie', 'Arnold', 'Arya', 'Ash', 'Asher', 'Ashleigh', 'Aspen',
'Astrid', 'Atlas', 'Atticus', 'Aubie', 'Augie', 'Autumn', 'Ava',
'Axel', 'Bailey', 'Baloo', 'Balto', 'Banditt', 'Banjo', 'Barclay',
'Barney', 'Baron', 'Barry', 'Batdog', 'Bauer', 'Baxter', 'Bayley',
'BeBe', 'Bear', 'Beau', 'Beckham', 'Beebop', 'Beemo', 'Bell',
'Bella', 'Belle', 'Ben', 'Benedict', 'Benji', 'Benny', 'Bentley',
'Berb', 'Berkeley', 'Bernie', 'Bert', 'Bertson', 'Betty', 'Beya',
'Biden', 'Bilbo', 'Billl', 'Billy', 'Binky', 'Birf', 'Bisquick',
'Blakely', 'Blanket', 'Blipson', 'Blitz', 'Bloo', 'Bloop', 'Blu',
'Blue', 'Bluebert', 'Bo', 'Bob', 'Bobb', 'Bobbay', 'Bobble',
'Bobby', 'Bode', 'Bodie', 'Bonaparte', 'Bones', 'Bookstore',
'Boomer', 'Boots', 'Boston', 'Bowie', 'Brad', 'Bradlay', 'Bradley',
'Brady', 'Brandi', 'Brandonald', 'Brandy', 'Brat', 'Brian',
'Brockly', 'Brody', 'Bronte', 'Brooks', 'Brownie', 'Bruce',
'Brudge', 'Bruiser', 'Bruno', 'Brutus', 'Bubba', 'Bubbles',
'Buckley', 'Buddah', 'Buddy', 'Bungalo', 'Burt', 'Butter',
'Butters', 'Cal', 'Calbert', 'Cali', 'Callie', 'Calvin', 'Canela',
'Cannon', 'Carbon', 'Carl', 'Carll', 'Carly', 'Carper', 'Carter',
'Caryl', 'Cash', 'Cassie', 'CeCe', 'Cecil', 'Cedrick', 'Cermet',
'Chadrick', 'Champ', 'Charl', 'Charles', 'Charleson', 'Charlie',
'Chase', 'Chaz', 'Cheesy', 'Chef', 'Chelsea', 'Cheryl', 'Chesney',
'Chester', 'Chesterson', 'Chet', 'Chevy', 'Chip', 'Chipson',
'Chloe', 'Chompsky', 'Christoper', 'Chubbs', 'Chuck', 'Chuckles',
'Chuq', 'Churlie', 'Cilantro', 'Clarence', 'Clark', 'Clarkus',
'Clarq', 'Claude', 'Cleopatricia', 'Clifford', 'Clybe', 'Clyde',
'Coco', 'Cody', 'Colby', 'Coleman', 'Colin', 'Combo', 'Comet',
'Cooper', 'Coops', 'Coopson', 'Cora', 'Corey', 'Covach', 'Craig',
'Crawford', 'Creg', 'Crimson', 'Crouton', 'Crumpet', 'Crystal',
'Cuddles', 'Cupcake', 'Cupid', 'Curtis', 'Daisy', 'Dakota', 'Dale',
'Dallas', 'Damon', 'Daniel', 'Danny', 'Dante', 'Darby', 'Darla',
'Darrel', 'Dash', 'Dave', 'Davey', 'Dawn', 'DayZ', 'Deacon',
'Derby', 'Derek', 'Devón', 'Dewey', 'Dex', 'Dexter', 'Dido',
'Dietrich', 'Diogi', 'Divine', 'Dixie', 'Django', 'Dobby', 'Doc',
'DonDon', 'Donny', 'Doobert', 'Dook', 'Dot', 'Dotsy', 'Doug',
'Duchess', 'Duddles', 'Dudley', 'Dug', 'Duke', 'Dunkin', 'Durg',
'Dutch', 'Dwight', 'Dylan', 'Earl', 'Eazy', 'Ebby', 'Ed', 'Edd',
'Edgar', 'Edmund', 'Eevee', 'Einstein', 'Eleanor', 'Eli', 'Ellie',
'Elliot', 'Emanuel', 'Ember', 'Emma', 'Emmie', 'Emmy', 'Enchilada',
'Erik', 'Eriq', 'Ester', 'Eugene', 'Eve', 'Evy', 'Fabio', 'Farfle',
'Ferg', 'Fido', 'Fiji', 'Fillup', 'Filup', 'Finley', 'Finn',
'Finnegus', 'Fiona', 'Fizz', 'Flash', 'Fletcher', 'Florence',
'Flurpson', 'Flávio', 'Frank', 'Frankie', 'Franklin', 'Franq',
'Fred', 'Freddery', 'Frönq', 'Furzey', 'Fwed', 'Fynn', 'Gabby',
'Gabe', 'Gary', 'General', 'Genevieve', 'Geno', 'Geoff', 'George',
'Georgie', 'Gerald', 'Gerbald', 'Gert', 'Gidget', 'Gilbert', 'Gin',
'Ginger', 'Gizmo', 'Glacier', 'Glenn', 'Godi', 'Godzilla',
'Goliath', 'Goose', 'Gordon', 'Grady', 'Grey', 'Griffin',
'Griswold', 'Grizz', 'Grizzie', 'Grizzwald', 'Gromit', 'Gunner',
'Gus', 'Gustaf', 'Gustav', 'Gòrdón', 'Hall', 'Halo', 'Hammond',
'Hamrick', 'Hank', 'Hanz', 'Happy', 'Harlso', 'Harnold', 'Harold',
'Harper', 'Harrison', 'Harry', 'Harvey', 'Hazel', 'Hector',
'Heinrich', 'Henry', 'Herald', 'Herb', 'Hercules', 'Herm',
'Hermione', 'Hero', 'Herschel', 'Hobbes', 'Holly', 'Horace',
'Howie', 'Hubertson', 'Huck', 'Humphrey', 'Hunter', 'Hurley',
'Huxley', 'Iggy', 'Ike', 'Indie', 'Iroh', 'Ito', 'Ivar', 'Izzy',
'JD', 'Jack', 'Jackie', 'Jackson', 'Jameson', 'Jamesy', 'Jangle',
'Jareld', 'Jarod', 'Jarvis', 'Jaspers', 'Jax', 'Jay', 'Jaycob',
'Jazz', 'Jazzy', 'Jeb', 'Jebberson', 'Jed', 'Jeffrey', 'Jeffri',
'Jeffrie', 'Jennifur', 'Jeph', 'Jeremy', 'Jerome', 'Jerry',
'Jersey', 'Jesse', 'Jessifer', 'Jessiga', 'Jett', 'Jim', 'Jimbo',
'Jiminus', 'Jiminy', 'Jimison', 'Jimothy', 'Jo', 'Jockson', 'Joey',
'Jomathan', 'Jonah', 'Jordy', 'Josep', 'Joshwa', 'Juckson', 'Julio',
'Julius', 'Juno', 'Kaia', 'Kaiya', 'Kallie', 'Kane', 'Kanu', 'Kara',
'Karl', 'Karll', 'Karma', 'Kathmandu', 'Katie', 'Kawhi', 'Kayla',
'Keet', 'Keith', 'Kellogg', 'Ken', 'Kendall', 'Kenneth', 'Kenny',
'Kenzie', 'Keurig', 'Kevin', 'Kevon', 'Kial', 'Kilo', 'Kingsley',
'Kirby', 'Kirk', 'Klein', 'Klevin', 'Kloey', 'Kobe', 'Koda', 'Kody',
'Koko', 'Kollin', 'Kona', 'Kota', 'Kramer', 'Kreg', 'Kreggory',
'Kulet', 'Kuyu', 'Kyle', 'Kyro', 'Lacy', 'Laela', 'Laika',
'Lambeau', 'Lance', 'Larry', 'Lassie', 'Layla', 'Leela', 'Lennon',
'Lenny', 'Lenox', 'Leo', 'Leonard', 'Leonidas', 'Levi', 'Liam',
'Lilah', 'Lili', 'Lilli', 'Lillie', 'Lilly', 'Lily', 'Lincoln',
'Linda', 'Link', 'Linus', 'Lipton', 'Livvie', 'Lizzie', 'Logan',
'Loki', 'Lola', 'Lolo', 'Longfellow', 'Loomis', 'Lorelei',
'Lorenzo', 'Lou', 'Louie', 'Louis', 'Luca', 'Lucia', 'Lucky',
'Lucy', 'Lugan', 'Lulu', 'Luna', 'Lupe', 'Luther', 'Mabel', 'Mac',
'Mack', 'Maddie', 'Maggie', 'Mairi', 'Maisey', 'Major', 'Maks',
'Malcolm', 'Malikai', 'Margo', 'Mark', 'Marlee', 'Marley', 'Marq',
'Marty', 'Marvin', 'Mary', 'Mason', 'Mattie', 'Maude', 'Mauve',
'Max', 'Maxaroni', 'Maximus', 'Maxwell', 'Maya', 'Meatball',
'Meera', 'Meyer', 'Mia', 'Michelangelope', 'Miguel', 'Mike',
'Miley', 'Milky', 'Millie', 'Milo', 'Mimosa', 'Mingus', 'Mister',
'Misty', 'Mitch', 'Mo', 'Moe', 'Mojo', 'Mollie', 'Molly', 'Mona',
'Monkey', 'Monster', 'Monty', 'Moofasa', 'Mookie', 'Moose',
'Moreton', 'Mosby', 'Murphy', 'Mutt', 'Mya', 'Nala', 'Naphaniel',
'Napolean', 'Nelly', 'Neptune', 'Newt', 'Nico', 'Nida', 'Nigel',
'Nimbus', 'Noah', 'Nollie', 'None', 'Noosh', 'Norman', 'Nugget',
'O', 'Oakley', 'Obi', 'Obie', 'Oddie', 'Odie', 'Odin', 'Olaf',
'Ole', 'Olive', 'Oliver', 'Olivia', 'Oliviér', 'Ollie', 'Opal',
'Opie', 'Oreo', 'Orion', 'Oscar', 'Oshie', 'Otis', 'Ozzie', 'Ozzy',
'Pablo', 'Paisley', 'Pancake', 'Panda', 'Patch', 'Patrick', 'Paull',
'Pavlov', 'Pawnd', 'Peaches', 'Peanut', 'Penelope', 'Penny',
'Pepper', 'Percy', 'Perry', 'Pete', 'Petrick', 'Pherb', 'Phil',
'Philbert', 'Philippe', 'Phineas', 'Phred', 'Pickles', 'Pilot',
'Pinot', 'Pip', 'Piper', 'Pippa', 'Pippin', 'Pipsy', 'Pluto',
'Poppy', 'Pubert', 'Puff', 'Pumpkin', 'Pupcasso', 'Quinn', 'Ralf',
'Ralph', 'Ralpher', 'Ralphie', 'Ralphson', 'Ralphus', 'Ralphy',
'Ralphé', 'Rambo', 'Randall', 'Raphael', 'Rascal', 'Raymond',
'Reagan', 'Reese', 'Reggie', 'Reginald', 'Remington', 'Remus',
'Remy', 'Reptar', 'Rey', 'Rhino', 'Richie', 'Ricky', 'Ridley',
'Riley', 'Rilo', 'Rinna', 'River', 'Rizzo', 'Rizzy', 'Robin',
'Rocco', 'Rocky', 'Rodman', 'Rodney', 'Rolf', 'Romeo', 'Ron',
'Ronduh', 'Ronnie', 'Rontu', 'Rooney', 'Roosevelt', 'Rorie', 'Rory',
'Roscoe', 'Rose', 'Rosie', 'Rover', 'Rubio', 'Ruby', 'Rudy',
'Rueben', 'Ruffles', 'Rufio', 'Rufus', 'Rumble', 'Rumpole',
'Rupert', 'Rusty', 'Sadie', 'Sage', 'Sailer', 'Sailor', 'Sam',
'Sammy', 'Sampson', 'Samsom', 'Samson', 'Sandra', 'Sandy', 'Sansa',
'Sarge', 'Saydee', 'Schnitzel', 'Schnozz', 'Scooter', 'Scott',
'Scout', 'Scruffers', 'Seamus', 'Sebastian', 'Sephie', 'Severus',
'Shadoe', 'Shadow', 'Shaggy', 'Shakespeare', 'Shawwn', 'Shelby',
'Shikha', 'Shiloh', 'Shnuggles', 'Shooter', 'Siba', 'Sid', 'Sierra',
'Simba', 'Skittle', 'Skittles', 'Sky', 'Skye', 'Smiley', 'Smokey',
'Snickers', 'Snicku', 'Snoop', 'Snoopy', 'Sobe', 'Socks',
'Sojourner', 'Solomon', 'Sonny', 'Sophie', 'Sora', 'Spanky',
'Spark', 'Sparky', 'Spencer', 'Sprinkles', 'Sprout', 'Staniel',
'Stanley', 'Stark', 'Stefan', 'Stella', 'Stephan', 'Stephanus',
'Steve', 'Steven', 'Stewie', 'Storkson', 'Stormy', 'Strider',
'Striker', 'Strudel', 'Stu', 'Stuart', 'Stubert', 'Sugar', 'Suki',
'Sully', 'Sundance', 'Sunny', 'Sunshine', 'Superpup', 'Swagger',
'Sweet', 'Sweets', 'Taco', 'Tango', 'Tanner', 'Tassy', 'Tater',
'Tayzie', 'Taz', 'Tebow', 'Ted', 'Tedders', 'Teddy', 'Tedrick',
'Terrance', 'Terrenth', 'Terry', 'Tess', 'Tessa', 'Theo',
'Theodore', 'Thor', 'Thumas', 'Tiger', 'Tilly', 'Timber', 'Timison',
'Timmy', 'Timofy', 'Tino', 'Titan', 'Tito', 'Tobi', 'Toby', 'Todo',
'Toffee', 'Tom', 'Tommy', 'Tonks', 'Torque', 'Tove', 'Travis',
'Traviss', 'Trevith', 'Trigger', 'Trip', 'Tripp', 'Trooper', 'Tuck',
'Tucker', 'Tuco', 'Tug', 'Tupawc', 'Tycho', 'Tyr', 'Tyrone',
'Tyrus', 'Ulysses', 'Venti', 'Vince', 'Vincent', 'Vinnie',
'Vinscent', 'Vixen', 'Wafer', 'Waffles', 'Walker', 'Wallace',
'Wally', 'Walter', 'Watson', 'Wesley', 'Wiggles', 'Willem',
'William', 'Willie', 'Willow', 'Willy', 'Wilson', 'Winifred',
'Winnie', 'Winston', 'Wishes', 'Wyatt', 'Yoda', 'Yogi', 'Yukon',
'Zara', 'Zeek', 'Zeke', 'Zeus', 'Ziva', 'Zoe', 'Zoey', 'Zooey',
'Zuzu', 'a', 'actually', 'all', 'an', 'by', 'getting', 'his',
'incredibly', 'infuriating', 'just', 'life', 'light', 'mad', 'my',
'not', 'officially', 'old', 'one', 'quite', 'space', 'such', 'the',
'this', 'unacceptable', 'very'], dtype=object)
There are some other error names: 'a', 'actually', 'all', 'an', 'by', 'getting', 'his', 'incredibly', 'infuriating', 'just', 'life', 'light', 'mad', 'my', 'not', 'officially', 'old', 'one', 'quite', 'space', 'such', 'the', 'this', 'unacceptable' and 'very'.
# Check how many name starting with a lowercase
len(df_twitter.loc[(df_twitter['name'].str.islower())])
109
df_twitter.loc[(df_twitter['name'].str.islower())].loc[:,['name','text']]
| name | text | |
|---|---|---|
| 22 | such | I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba |
| 56 | a | Here is a pupper approaching maximum borkdrive. Zooming at never before seen speeds. 14/10 paw-inspiring af \n(IG: puffie_the_chow) https://t.co/ghXBIIeQZF |
| 118 | quite | RT @dog_rates: We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10… |
| 169 | quite | We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10 https://t.co/g2nSyGenG9 |
| 193 | quite | Guys, we only rate dogs. This is quite clearly a bulbasaur. Please only send dogs. Thank you... 12/10 human used pet, it's super effective https://t.co/Xc7uj1C64x |
| 335 | not | There's going to be a dog terminal at JFK Airport. This is not a drill. 10/10 \nhttps://t.co/dp5h9bCwU7 |
| 369 | one | Occasionally, we're sent fantastic stories. This is one of them. 14/10 for Grace https://t.co/bZ4axuH6OK |
| 542 | incredibly | We only rate dogs. Please stop sending in non-canines like this Freudian Poof Lion. This is incredibly frustrating... 11/10 https://t.co/IZidSrBvhi |
| 649 | a | Here is a perfect example of someone who has their priorities in order. 13/10 for both owner and Forrest https://t.co/LRyMrU7Wfq |
| 682 | mad | RT @dog_rates: Say hello to mad pupper. You know what you did. 13/10 would pet until no longer furustrated https://t.co/u1ulQ5heLX |
| 759 | an | RT @dog_rates: This is an East African Chalupa Seal. We only rate dogs. Please only send in dogs. Thank you... 10/10 https://t.co/iHe6liLwWR |
| 773 | very | RT @dog_rates: We only rate dogs. Pls stop sending in non-canines like this Mongolian grass snake. This is very frustrating. 11/10 https://… |
| 801 | a | Guys this is getting so out of hand. We only rate dogs. This is a Galapagos Speed Panda. Pls only send dogs... 10/10 https://t.co/8lpAGaZRFn |
| 819 | very | We only rate dogs. Pls stop sending in non-canines like this Arctic Floof Kangaroo. This is very frustrating. 11/10 https://t.co/qlUDuPoE3d |
| 822 | just | RT @dog_rates: This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC |
| 852 | my | This is my dog. Her name is Zoey. She knows I've been rating other dogs. She's not happy. 13/10 no bias at all https://t.co/ep1NkYoiwB |
| 924 | one | This is one of the most inspirational stories I've ever come across. I have no words. 14/10 for both doggo and owner https://t.co/I5ld3eKD5k |
| 988 | not | What jokester sent in a pic without a dog in it? This is not @rock_rates. This is @dog_rates. Thank you ...10/10 https://t.co/nDPaYHrtNX |
| 992 | his | That is Quizno. This is his beach. He does not tolerate human shenanigans on his beach. 10/10 reclaim ur land doggo https://t.co/vdr7DaRSa7 |
| 993 | one | This is one of the most reckless puppers I've ever seen. How she got a license in the first place is beyond me. 6/10 https://t.co/z5bAdtn9kd |
| 1002 | a | This is a mighty rare blue-tailed hammer sherk. Human almost lost a limb trying to take these. Be careful guys. 8/10 https://t.co/TGenMeXreW |
| 1004 | a | Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R |
| 1017 | a | This is a carrot. We only rate dogs. Please only send in dogs. You all really should know this by now ...11/10 https://t.co/9e48aPrBm2 |
| 1025 | an | This is an Iraqi Speed Kangaroo. It is not a dog. Please only send in dogs. I'm very angry with all of you ...9/10 https://t.co/5qpBTTpgUt |
| 1031 | very | We only rate dogs. Pls stop sending in non-canines like this Jamaican Flop Seal. This is very very frustrating. 9/10 https://t.co/nc53zEN0hZ |
| 1040 | actually | This is actually a pupper and I'd pet it so well. 12/10\nhttps://t.co/RNqS7C4Y4N |
| 1049 | a | This is a very rare Great Alaskan Bush Pupper. Hard to stumble upon without spooking. 12/10 would pet passionately https://t.co/xOBKCdpzaa |
| 1063 | just | This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC |
| 1071 | getting | This is getting incredibly frustrating. This is a Mexican Golden Beaver. We only rate dogs. Only send dogs ...10/10 https://t.co/0yolOOyD3X |
| 1095 | mad | Say hello to mad pupper. You know what you did. 13/10 would pet until no longer furustrated https://t.co/u1ulQ5heLX |
| ... | ... | ... |
| 2191 | a | This is a Slovakian Helter Skelter Feta named Leroi. Likes to skip on roofs. Good traction. Much balance. 10/10 wow! https://t.co/Dmy2mY2Qj5 |
| 2198 | a | This is a wild Toblerone from Papua New Guinea. Mouth always open. Addicted to hay. Acts blind. 7/10 handsome dog https://t.co/IGmVbz07tZ |
| 2204 | an | This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg |
| 2211 | a | Here is a horned dog. Much grace. Can jump over moons (dam!). Paws not soft. Bad at barking. 7/10 can still pet tho https://t.co/2Su7gmsnZm |
| 2212 | the | Never forget this vine. You will not stop watching for at least 15 minutes. This is the second coveted.. 13/10 https://t.co/roqIxCvEB3 |
| 2218 | a | This is a Birmingham Quagmire named Chuk. Loves to relax and watch the game while sippin on that iced mocha. 10/10 https://t.co/HvNg9JWxFt |
| 2222 | a | Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m |
| 2235 | a | This is a Trans Siberian Kellogg named Alfonso. Huge ass eyeballs. Actually Dobby from Harry Potter. 7/10 https://t.co/XpseHBlAAb |
| 2249 | a | This is a Shotokon Macadamia mix named Cheryl. Sophisticated af. Looks like a disappointed librarian. Shh (lol) 9/10 https://t.co/J4GnJ5Swba |
| 2255 | a | This is a rare Hungarian Pinot named Jessiga. She is either mid-stroke or got stuck in the washing machine. 8/10 https://t.co/ZU0i0KJyqD |
| 2264 | a | This is a southwest Coriander named Klint. Hat looks expensive. Still on house arrest :(\n9/10 https://t.co/IQTOMqDUIe |
| 2273 | a | This is a northern Wahoo named Kohl. He runs this town. Chases tumbleweeds. Draws gun wicked fast. 11/10 legendary https://t.co/J4vn2rOYFk |
| 2287 | a | This is a Dasani Kingfisher from Maine. His name is Daryl. Daryl doesn't like being swallowed by a panda. 8/10 https://t.co/jpaeu6LNmW |
| 2304 | a | This is a curly Ticonderoga named Pepe. No feet. Loves to jet ski. 11/10 would hug until forever https://t.co/cyDfaK8NBc |
| 2311 | a | This is a purebred Bacardi named Octaviath. Can shoot spaghetti out of mouth. 10/10 https://t.co/uEvsGLOFHa |
| 2314 | a | This is a golden Buckminsterfullerene named Johm. Drives trucks. Lumberjack (?). Enjoys wall. 8/10 would hug softly https://t.co/uQbZJM2DQB |
| 2326 | quite | This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t |
| 2327 | a | This is a southern Vesuvius bumblegruff. Can drive a truck (wow). Made friends with 5 other nifty dogs (neat). 7/10 https://t.co/LopTBkKa8h |
| 2333 | an | This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL |
| 2334 | a | This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0 |
| 2335 | an | This is an Albanian 3 1/2 legged Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv |
| 2345 | the | This is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehK |
| 2346 | the | Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p |
| 2347 | a | My oh my. This is a rare blond Canadian terrier on wheels. Only $8.98. Rather docile. 9/10 very rare https://t.co/yWBqbrzy8O |
| 2348 | a | Here is a Siberian heavily armored polar bear mix. Strong owner. 10/10 I would do unspeakable things to pet this dog https://t.co/rdivxLiqEt |
| 2349 | an | This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc |
| 2350 | a | This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe |
| 2352 | a | This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx |
| 2353 | a | Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR |
| 2354 | a | This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI |
109 rows × 2 columns
Filtering by names starting with lowercase, looks like there is an error with the name caching algorithm as it expected a name after phrases like: "This is", "Here is", "that is" or "Say hello to". As we can see in the majority of the sample texts of each tweets, the name doesn't show. Nevertheless, for some other tweet's texts, we can see the real name after phrases such as: 'Her name is', 'His name is', 'That is' and 'named'.
# Check the doggo stage
df_twitter.doggo.value_counts()
None 2259 doggo 97 Name: doggo, dtype: int64
# Check the floofer stage
df_twitter.floofer.value_counts()
None 2346 floofer 10 Name: floofer, dtype: int64
# Check the puppo stage
df_twitter.puppo.value_counts()
None 2326 puppo 30 Name: puppo, dtype: int64
# Check the pupper stage
df_twitter.pupper.value_counts()
None 2099 pupper 257 Name: pupper, dtype: int64
# Check if there are records with more than one "dog stage" for the same tweet_id
# Doggo and floofer
print("Number of records with both doggo and floofer stage", len(df_twitter[(df_twitter.doggo != 'None') & (df_twitter.floofer != 'None')]))
# Doggo and pupper
print("Number of records with both doggo and pupper stage", len(df_twitter[(df_twitter.doggo != 'None') & (df_twitter.pupper != 'None')]))
# Doggo and puppo
print("Number of records with both doggo and puppo stage", len(df_twitter[(df_twitter.doggo != 'None') & (df_twitter.puppo != 'None')]))
# floofer and pupper
print("Number of records with both floofer and pupper stage", len(df_twitter[(df_twitter.floofer != 'None') & (df_twitter.pupper != 'None')]))
# floofer and puppo
print("Number of records with both floofer and puppo stage", len(df_twitter[(df_twitter.floofer != 'None') & (df_twitter.puppo != 'None')]))
# pupper and puppo
print("Number of records with both pupper and puppo stage", len(df_twitter[(df_twitter.pupper != 'None') & (df_twitter.puppo != 'None')]))
Number of records with both doggo and floofer stage 1 Number of records with both doggo and pupper stage 12 Number of records with both doggo and puppo stage 1 Number of records with both floofer and pupper stage 0 Number of records with both floofer and puppo stage 0 Number of records with both pupper and puppo stage 0
Let's look deeper into the records with more than one stage:
# Check the tweet text
df_twitter[(df_twitter.doggo != 'None') & (df_twitter.floofer != 'None')]
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | rating_numerator | rating_denominator | name | doggo | floofer | pupper | puppo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | 854010172552949760 | NaN | NaN | 2017-04-17 16:34:26 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | At first I thought this was a shy doggo, but it's actually a Rare Canadian Floofer Owl. Amateurs would confuse the two. 11/10 only send dogs https://t.co/TXdT3tmuYk | NaN | NaN | NaN | https://twitter.com/dog_rates/status/854010172552949760/photo/1,https://twitter.com/dog_rates/status/854010172552949760/photo/1 | 11 | 10 | None | doggo | floofer | None | None |
As can be read in the text, this dog is a Floofer.
df_twitter[(df_twitter.doggo != 'None') & (df_twitter.pupper != 'None')]
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | rating_numerator | rating_denominator | name | doggo | floofer | pupper | puppo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 460 | 817777686764523521 | NaN | NaN | 2017-01-07 16:59:28 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Dido. She's playing the lead role in "Pupper Stops to Catch Snow Before Resuming Shadow Box with Dried Apple." 13/10 (IG: didodoggo) https://t.co/m7isZrOBX7 | NaN | NaN | NaN | https://twitter.com/dog_rates/status/817777686764523521/video/1 | 13 | 10 | Dido | doggo | None | pupper | None |
| 531 | 808106460588765185 | NaN | NaN | 2016-12-12 00:29:28 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Here we have Burke (pupper) and Dexter (doggo). Pupper wants to be exactly like doggo. Both 12/10 would pet at same time https://t.co/ANBpEYHaho | NaN | NaN | NaN | https://twitter.com/dog_rates/status/808106460588765185/photo/1 | 12 | 10 | None | doggo | None | pupper | None |
| 565 | 802265048156610565 | 7.331095e+17 | 4.196984e+09 | 2016-11-25 21:37:47 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Like doggo, like pupper version 2. Both 11/10 https://t.co/9IxWAXFqze | NaN | NaN | NaN | https://twitter.com/dog_rates/status/802265048156610565/photo/1 | 11 | 10 | None | doggo | None | pupper | None |
| 575 | 801115127852503040 | NaN | NaN | 2016-11-22 17:28:25 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Bones. He's being haunted by another doggo of roughly the same size. 12/10 deep breaths pupper everything's fine https://t.co/55Dqe0SJNj | NaN | NaN | NaN | https://twitter.com/dog_rates/status/801115127852503040/photo/1,https://twitter.com/dog_rates/status/801115127852503040/photo/1 | 12 | 10 | Bones | doggo | None | pupper | None |
| 705 | 785639753186217984 | NaN | NaN | 2016-10-11 00:34:48 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Pinot. He's a sophisticated doggo. You can tell by the hat. Also pointier than your average pupper. Still 10/10 would pet cautiously https://t.co/f2wmLZTPHd | NaN | NaN | NaN | https://twitter.com/dog_rates/status/785639753186217984/photo/1,https://twitter.com/dog_rates/status/785639753186217984/photo/1 | 10 | 10 | Pinot | doggo | None | pupper | None |
| 733 | 781308096455073793 | NaN | NaN | 2016-09-29 01:42:20 +0000 | <a href="http://vine.co" rel="nofollow">Vine - Make a Scene</a> | Pupper butt 1, Doggo 0. Both 12/10 https://t.co/WQvcPEpH2u | NaN | NaN | NaN | https://vine.co/v/5rgu2Law2ut | 12 | 10 | None | doggo | None | pupper | None |
| 778 | 775898661951791106 | NaN | NaN | 2016-09-14 03:27:11 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | RT @dog_rates: Like father (doggo), like son (pupper). Both 12/10 https://t.co/pG2inLaOda | 7.331095e+17 | 4.196984e+09 | 2016-05-19 01:38:16 +0000 | https://twitter.com/dog_rates/status/733109485275860992/photo/1,https://twitter.com/dog_rates/status/733109485275860992/photo/1 | 12 | 10 | None | doggo | None | pupper | None |
| 822 | 770093767776997377 | NaN | NaN | 2016-08-29 03:00:36 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | RT @dog_rates: This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC | 7.410673e+17 | 4.196984e+09 | 2016-06-10 00:39:48 +0000 | https://twitter.com/dog_rates/status/741067306818797568/photo/1,https://twitter.com/dog_rates/status/741067306818797568/photo/1 | 12 | 10 | just | doggo | None | pupper | None |
| 889 | 759793422261743616 | NaN | NaN | 2016-07-31 16:50:42 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Meet Maggie & Lila. Maggie is the doggo, Lila is the pupper. They are sisters. Both 12/10 would pet at the same time https://t.co/MYwR4DQKll | NaN | NaN | NaN | https://twitter.com/dog_rates/status/759793422261743616/photo/1,https://twitter.com/dog_rates/status/759793422261743616/photo/1 | 12 | 10 | Maggie | doggo | None | pupper | None |
| 956 | 751583847268179968 | NaN | NaN | 2016-07-09 01:08:47 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8 | NaN | NaN | NaN | https://twitter.com/dog_rates/status/751583847268179968/photo/1 | 5 | 10 | None | doggo | None | pupper | None |
| 1063 | 741067306818797568 | NaN | NaN | 2016-06-10 00:39:48 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC | NaN | NaN | NaN | https://twitter.com/dog_rates/status/741067306818797568/photo/1 | 12 | 10 | just | doggo | None | pupper | None |
| 1113 | 733109485275860992 | NaN | NaN | 2016-05-19 01:38:16 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Like father (doggo), like son (pupper). Both 12/10 https://t.co/pG2inLaOda | NaN | NaN | NaN | https://twitter.com/dog_rates/status/733109485275860992/photo/1 | 12 | 10 | None | doggo | None | pupper | None |
Looking at the text and also folowwing the link, I've determined if the tweets rate two dogs or one, getting for this last case its stage:
| tweet_id | 2 dogs / 1 dog (Stage) |
|---|---|
| 817777686764523521 | Pupper |
| 808106460588765185 | 2 dogs |
| 802265048156610565 | 2 dogs |
| 801115127852503040 | Doggo |
| 785639753186217984 | Doggo |
| 781308096455073793 | 2 dogs |
| 775898661951791106 | 2 dogs |
| 770093767776997377 | 2 dogs |
| 759793422261743616 | 2 dogs |
| 751583847268179968 | doggo |
| 741067306818797568 | 2 dogs |
| 733109485275860992 | 2 dogs |
df_twitter[(df_twitter.doggo != 'None') & (df_twitter.puppo != 'None')]
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | rating_numerator | rating_denominator | name | doggo | floofer | pupper | puppo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 191 | 855851453814013952 | NaN | NaN | 2017-04-22 18:31:02 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Here's a puppo participating in the #ScienceMarch. Cleverly disguising her own doggo agenda. 13/10 would keep the planet habitable for https://t.co/cMhq16isel | NaN | NaN | NaN | https://twitter.com/dog_rates/status/855851453814013952/photo/1 | 13 | 10 | None | doggo | None | None | puppo |
As can be read in the text, this dog is a Puppo.
# Check the different values for the numerator
df_twitter.rating_numerator.value_counts()
12 558 11 464 10 461 13 351 9 158 8 102 7 55 14 54 5 37 6 32 3 19 4 17 1 9 2 9 420 2 0 2 15 2 75 2 80 1 20 1 24 1 26 1 44 1 50 1 60 1 165 1 84 1 88 1 144 1 182 1 143 1 666 1 960 1 1776 1 17 1 27 1 45 1 99 1 121 1 204 1 Name: rating_numerator, dtype: int64
# Check the different values for the denominator
df_twitter.rating_denominator.value_counts()
10 2333 11 3 50 3 80 2 20 2 2 1 16 1 40 1 70 1 15 1 90 1 110 1 120 1 130 1 150 1 170 1 7 1 0 1 Name: rating_denominator, dtype: int64
# Sort the numerator values
np.sort(df_twitter['rating_numerator'].unique())
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 17, 20, 24, 26, 27, 44,
45, 50, 60, 75, 80, 84, 88, 99, 121, 143, 144,
165, 182, 204, 420, 666, 960, 1776])
df_twitter[df_twitter['rating_numerator']>=26]
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | rating_numerator | rating_denominator | name | doggo | floofer | pupper | puppo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 188 | 855862651834028034 | 8.558616e+17 | 1.943518e+08 | 2017-04-22 19:15:32 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | @dhmontgomery We also gave snoop dogg a 420/10 but I think that predated your research | NaN | NaN | NaN | NaN | 420 | 10 | None | None | None | None | None |
| 189 | 855860136149123072 | 8.558585e+17 | 1.361572e+07 | 2017-04-22 19:05:32 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | @s8n You tried very hard to portray this good boy as not so good, but you have ultimately failed. His goodness shines through. 666/10 | NaN | NaN | NaN | NaN | 666 | 10 | None | None | None | None | None |
| 290 | 838150277551247360 | 8.381455e+17 | 2.195506e+07 | 2017-03-04 22:12:52 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | @markhoppus 182/10 | NaN | NaN | NaN | NaN | 182 | 10 | None | None | None | None | None |
| 313 | 835246439529840640 | 8.352460e+17 | 2.625958e+07 | 2017-02-24 21:54:03 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | @jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho | NaN | NaN | NaN | NaN | 960 | 0 | None | None | None | None | None |
| 340 | 832215909146226688 | NaN | NaN | 2017-02-16 13:11:49 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | RT @dog_rates: This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wu… | 7.867091e+17 | 4.196984e+09 | 2016-10-13 23:23:56 +0000 | https://twitter.com/dog_rates/status/786709082849828864/photo/1 | 75 | 10 | Logan | None | None | None | None |
| 433 | 820690176645140481 | NaN | NaN | 2017-01-15 17:52:40 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd | NaN | NaN | NaN | https://twitter.com/dog_rates/status/820690176645140481/photo/1,https://twitter.com/dog_rates/status/820690176645140481/photo/1,https://twitter.com/dog_rates/status/820690176645140481/photo/1 | 84 | 70 | None | None | None | None | None |
| 695 | 786709082849828864 | NaN | NaN | 2016-10-13 23:23:56 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS | NaN | NaN | NaN | https://twitter.com/dog_rates/status/786709082849828864/photo/1 | 75 | 10 | Logan | None | None | None | None |
| 763 | 778027034220126208 | NaN | NaN | 2016-09-20 00:24:34 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq | NaN | NaN | NaN | https://twitter.com/dog_rates/status/778027034220126208/photo/1 | 27 | 10 | Sophie | None | None | pupper | None |
| 902 | 758467244762497024 | NaN | NaN | 2016-07-28 01:00:57 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE | NaN | NaN | NaN | https://twitter.com/dog_rates/status/758467244762497024/video/1 | 165 | 150 | None | None | None | None | None |
| 979 | 749981277374128128 | NaN | NaN | 2016-07-04 15:00:45 +0000 | <a href="https://about.twitter.com/products/tweetdeck" rel="nofollow">TweetDeck</a> | This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh | NaN | NaN | NaN | https://twitter.com/dog_rates/status/749981277374128128/photo/1 | 1776 | 10 | Atticus | None | None | None | None |
| 1120 | 731156023742988288 | NaN | NaN | 2016-05-13 16:15:54 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv | NaN | NaN | NaN | https://twitter.com/dog_rates/status/731156023742988288/photo/1 | 204 | 170 | this | None | None | None | None |
| 1202 | 716439118184652801 | NaN | NaN | 2016-04-03 01:36:11 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq | NaN | NaN | NaN | https://twitter.com/dog_rates/status/716439118184652801/photo/1 | 50 | 50 | Bluebert | None | None | None | None |
| 1228 | 713900603437621249 | NaN | NaN | 2016-03-27 01:29:02 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1 | NaN | NaN | NaN | https://twitter.com/dog_rates/status/713900603437621249/photo/1 | 99 | 90 | None | None | None | None | None |
| 1254 | 710658690886586372 | NaN | NaN | 2016-03-18 02:46:49 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12 | NaN | NaN | NaN | https://twitter.com/dog_rates/status/710658690886586372/photo/1 | 80 | 80 | None | None | None | None | None |
| 1274 | 709198395643068416 | NaN | NaN | 2016-03-14 02:04:08 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | From left to right:\nCletus, Jerome, Alejandro, Burp, & Titson\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK | NaN | NaN | NaN | https://twitter.com/dog_rates/status/709198395643068416/photo/1 | 45 | 50 | None | None | None | None | None |
| 1351 | 704054845121142784 | NaN | NaN | 2016-02-28 21:25:30 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Here is a whole flock of puppers. 60/50 I'll take the lot https://t.co/9dpcw6MdWa | NaN | NaN | NaN | https://twitter.com/dog_rates/status/704054845121142784/photo/1 | 60 | 50 | a | None | None | None | None |
| 1433 | 697463031882764288 | NaN | NaN | 2016-02-10 16:51:59 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ | NaN | NaN | NaN | https://twitter.com/dog_rates/status/697463031882764288/photo/1 | 44 | 40 | None | None | None | None | None |
| 1634 | 684225744407494656 | 6.842229e+17 | 4.196984e+09 | 2016-01-05 04:11:44 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3 | NaN | NaN | NaN | https://twitter.com/dog_rates/status/684225744407494656/photo/1,https://twitter.com/dog_rates/status/684225744407494656/photo/1 | 143 | 130 | None | None | None | None | None |
| 1635 | 684222868335505415 | NaN | NaN | 2016-01-05 04:00:18 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55 | NaN | NaN | NaN | https://twitter.com/dog_rates/status/684222868335505415/photo/1 | 121 | 110 | None | None | None | None | None |
| 1712 | 680494726643068929 | NaN | NaN | 2015-12-25 21:06:00 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD | NaN | NaN | NaN | https://twitter.com/dog_rates/status/680494726643068929/photo/1 | 26 | 10 | None | None | None | None | None |
| 1779 | 677716515794329600 | NaN | NaN | 2015-12-18 05:06:23 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq | NaN | NaN | NaN | https://twitter.com/dog_rates/status/677716515794329600/photo/1 | 144 | 120 | None | None | None | None | None |
| 1843 | 675853064436391936 | NaN | NaN | 2015-12-13 01:41:41 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw | NaN | NaN | NaN | https://twitter.com/dog_rates/status/675853064436391936/photo/1,https://twitter.com/dog_rates/status/675853064436391936/photo/1 | 88 | 80 | None | None | None | None | None |
| 2074 | 670842764863651840 | NaN | NaN | 2015-11-29 05:52:33 +0000 | <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> | After so many requests... here you go.\n\nGood dogg. 420/10 https://t.co/yfAAo1gdeY | NaN | NaN | NaN | https://twitter.com/dog_rates/status/670842764863651840/photo/1 | 420 | 10 | None | None | None | None | None |
There are severals errors due to numerators with decimals
# Sort the denominator values
np.sort(df_twitter['rating_denominator'].unique())
array([ 0, 2, 7, 10, 11, 15, 16, 20, 40, 50, 70, 80, 90,
110, 120, 130, 150, 170])
df_twitter.rating_denominator.value_counts()
10 2333 11 3 50 3 80 2 20 2 2 1 16 1 40 1 70 1 15 1 90 1 110 1 120 1 130 1 150 1 170 1 7 1 0 1 Name: rating_denominator, dtype: int64
df_twitter[df_twitter['rating_denominator']!=10].loc[:,['text','rating_denominator']]
| text | rating_denominator | |
|---|---|---|
| 313 | @jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho | 0 |
| 342 | @docmisterio account started on 11/15/15 | 15 |
| 433 | The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd | 70 |
| 516 | Meet Sam. She smiles 24/7 & secretly aspires to be a reindeer. \nKeep Sam smiling by clicking and sharing this link:\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx | 7 |
| 784 | RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/… | 11 |
| 902 | Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE | 150 |
| 1068 | After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ | 11 |
| 1120 | Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv | 170 |
| 1165 | Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a | 20 |
| 1202 | This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq | 50 |
| 1228 | Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1 | 90 |
| 1254 | Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12 | 80 |
| 1274 | From left to right:\nCletus, Jerome, Alejandro, Burp, & Titson\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK | 50 |
| 1351 | Here is a whole flock of puppers. 60/50 I'll take the lot https://t.co/9dpcw6MdWa | 50 |
| 1433 | Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ | 40 |
| 1598 | Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating | 20 |
| 1634 | Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3 | 130 |
| 1635 | Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55 | 110 |
| 1662 | This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5 | 11 |
| 1663 | I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible | 16 |
| 1779 | IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq | 120 |
| 1843 | Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw | 80 |
| 2335 | This is an Albanian 3 1/2 legged Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv | 2 |
df_twitter[df_twitter['rating_denominator']==50].loc[:,['text','rating_denominator']]
| text | rating_denominator | |
|---|---|---|
| 1202 | This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq | 50 |
| 1274 | From left to right:\nCletus, Jerome, Alejandro, Burp, & Titson\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK | 50 |
| 1351 | Here is a whole flock of puppers. 60/50 I'll take the lot https://t.co/9dpcw6MdWa | 50 |
df_twitter.iloc[1165,:]
tweet_id 722974582966214656 in_reply_to_status_id NaN in_reply_to_user_id NaN timestamp 2016-04-21 02:25:47 +0000 source <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> text Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a retweeted_status_id NaN retweeted_status_user_id NaN retweeted_status_timestamp NaN expanded_urls https://twitter.com/dog_rates/status/722974582966214656/photo/1 rating_numerator 4 rating_denominator 20 name None doggo None floofer None pupper None puppo None Name: 1165, dtype: object
As we can see in the text of this tweet above, the denominator of 20 is due to the rating of 2 dogs.
df_img_pred.columns
Index(['tweet_id', 'jpg_url', 'img_num', 'p1', 'p1_conf', 'p1_dog', 'p2',
'p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog'],
dtype='object')
# Check for duplicated tweet_ids
sum(df_img_pred.tweet_id.duplicated())
0
# Check for duplicated tweets urls
sum(df_img_pred.jpg_url.duplicated())
66
# Check how many time are the jpg_urls duplicated
df_img_pred.jpg_url.value_counts()
https://pbs.twimg.com/media/CkNjahBXAAQ2kWo.jpg 2
https://pbs.twimg.com/ext_tw_video_thumb/807106774843039744/pu/img/8XZg1xW35Xp2J6JW.jpg 2
https://pbs.twimg.com/media/CuRDF-XWcAIZSer.jpg 2
https://pbs.twimg.com/media/Ct2qO5PXEAE6eB0.jpg 2
https://pbs.twimg.com/media/Cq9guJ5WgAADfpF.jpg 2
https://pbs.twimg.com/media/CtKHLuCWYAA2TTs.jpg 2
https://pbs.twimg.com/tweet_video_thumb/CeBym7oXEAEWbEg.jpg 2
https://pbs.twimg.com/media/DA7iHL5U0AA1OQo.jpg 2
https://pbs.twimg.com/media/C4KHj-nWQAA3poV.jpg 2
https://pbs.twimg.com/media/CUN4Or5UAAAa5K4.jpg 2
https://pbs.twimg.com/media/CtzKC7zXEAALfSo.jpg 2
https://pbs.twimg.com/media/CsVO7ljW8AAckRD.jpg 2
https://pbs.twimg.com/media/CtVAvX-WIAAcGTf.jpg 2
https://pbs.twimg.com/media/Cx5R8wPVEAALa9r.jpg 2
https://pbs.twimg.com/media/CmoPdmHW8AAi8BI.jpg 2
https://pbs.twimg.com/media/CV_cnjHWUAADc-c.jpg 2
https://pbs.twimg.com/media/Ct72q9jWcAAhlnw.jpg 2
https://pbs.twimg.com/media/CiyHLocU4AI2pJu.jpg 2
https://pbs.twimg.com/media/C2kzTGxWEAEOpPL.jpg 2
https://pbs.twimg.com/media/ChK1tdBWwAQ1flD.jpg 2
https://pbs.twimg.com/media/Cp6db4-XYAAMmqL.jpg 2
https://pbs.twimg.com/media/CzG425nWgAAnP7P.jpg 2
https://pbs.twimg.com/media/Cbs3DOAXIAAp3Bd.jpg 2
https://pbs.twimg.com/media/C4bTH6nWMAAX_bJ.jpg 2
https://pbs.twimg.com/media/Ck2d7tJWUAEPTL3.jpg 2
https://pbs.twimg.com/media/CcG07BYW0AErrC9.jpg 2
https://pbs.twimg.com/media/CVMOlMiWwAA4Yxl.jpg 2
https://pbs.twimg.com/media/CkjMx99UoAM2B1a.jpg 2
https://pbs.twimg.com/ext_tw_video_thumb/817423809049493505/pu/img/5OFW0yueFu9oTUiQ.jpg 2
https://pbs.twimg.com/media/C2oRbOuWEAAbVSl.jpg 2
..
https://pbs.twimg.com/media/CmAC7ehXEAAqSuW.jpg 1
https://pbs.twimg.com/media/DFnwSY4WAAAMliS.jpg 1
https://pbs.twimg.com/media/CUTDtyGXIAARxus.jpg 1
https://pbs.twimg.com/media/CWofOHUWUAACGVa.jpg 1
https://pbs.twimg.com/media/CV1WXsmWcAAgQ56.jpg 1
https://pbs.twimg.com/media/Cmjlsh1XgAEvhq_.jpg 1
https://pbs.twimg.com/media/Ce99GhLW8AAHG38.jpg 1
https://pbs.twimg.com/media/Cj1I1fbWYAAOwff.jpg 1
https://pbs.twimg.com/media/CmyPXNOW8AEtaJ-.jpg 1
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https://pbs.twimg.com/media/CXh5_dDWQAIbU-J.jpg 1
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https://pbs.twimg.com/ext_tw_video_thumb/762471745303355393/pu/img/RKcEUz7-VDipoGKJ.jpg 1
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https://pbs.twimg.com/media/CU8z65IUEAQBc4q.jpg 1
Name: jpg_url, Length: 2009, dtype: int64
# Check the different values for p1 and their frequency for each of them
df_img_pred.p1.value_counts()
golden_retriever 150
Labrador_retriever 100
Pembroke 89
Chihuahua 83
pug 57
chow 44
Samoyed 43
toy_poodle 39
Pomeranian 38
cocker_spaniel 30
malamute 30
French_bulldog 26
Chesapeake_Bay_retriever 23
miniature_pinscher 23
seat_belt 22
Siberian_husky 20
Staffordshire_bullterrier 20
German_shepherd 20
web_site 19
Cardigan 19
Eskimo_dog 18
Shetland_sheepdog 18
beagle 18
Maltese_dog 18
teddy 18
Shih-Tzu 17
Lakeland_terrier 17
Rottweiler 17
kuvasz 16
Italian_greyhound 16
..
carousel 1
sandbar 1
leopard 1
handkerchief 1
water_buffalo 1
minibus 1
sunglasses 1
three-toed_sloth 1
agama 1
bison 1
crash_helmet 1
padlock 1
groenendael 1
slug 1
toilet_seat 1
king_penguin 1
pitcher 1
EntleBucher 1
otter 1
ocarina 1
bighorn 1
desktop_computer 1
espresso 1
cougar 1
rotisserie 1
hare 1
robin 1
clog 1
pencil_box 1
guenon 1
Name: p1, Length: 378, dtype: int64
df_img_pred.p2.value_counts()
Labrador_retriever 104
golden_retriever 92
Cardigan 73
Chihuahua 44
Pomeranian 42
Chesapeake_Bay_retriever 41
French_bulldog 41
toy_poodle 37
cocker_spaniel 34
miniature_poodle 33
Siberian_husky 33
beagle 28
Eskimo_dog 27
Pembroke 27
collie 27
kuvasz 26
Italian_greyhound 22
American_Staffordshire_terrier 21
Pekinese 21
malinois 20
Samoyed 20
miniature_pinscher 20
chow 20
toy_terrier 20
Norwegian_elkhound 19
Boston_bull 19
Staffordshire_bullterrier 18
Irish_terrier 17
pug 17
kelpie 16
..
African_hunting_dog 1
can_opener 1
dugong 1
lampshade 1
bucket 1
snail 1
Gila_monster 1
shower_curtain 1
hand-held_computer 1
birdhouse 1
Japanese_spaniel 1
grey_whale 1
standard_schnauzer 1
jigsaw_puzzle 1
European_gallinule 1
window_shade 1
grey_fox 1
bow 1
trench_coat 1
iPod 1
minibus 1
Kerry_blue_terrier 1
dining_table 1
cliff 1
turnstile 1
mosquito_net 1
tick 1
common_newt 1
bagel 1
shower_cap 1
Name: p2, Length: 405, dtype: int64
df_img_pred.p3.value_counts()
Labrador_retriever 79
Chihuahua 58
golden_retriever 48
Eskimo_dog 38
kelpie 35
kuvasz 34
chow 32
Staffordshire_bullterrier 32
cocker_spaniel 31
beagle 31
Pomeranian 29
Pekinese 29
toy_poodle 29
Pembroke 27
Great_Pyrenees 27
Chesapeake_Bay_retriever 27
malamute 26
French_bulldog 26
American_Staffordshire_terrier 24
pug 23
Cardigan 23
basenji 21
toy_terrier 20
bull_mastiff 20
Siberian_husky 19
Boston_bull 17
Shetland_sheepdog 17
boxer 16
doormat 16
Lakeland_terrier 16
..
barbell 1
gar 1
theater_curtain 1
wild_boar 1
mitten 1
grand_piano 1
bow 1
balance_beam 1
bonnet 1
Kerry_blue_terrier 1
hammerhead 1
mongoose 1
cliff 1
mosquito_net 1
American_black_bear 1
cuirass 1
common_newt 1
loupe 1
grocery_store 1
can_opener 1
chain 1
banana 1
coral_reef 1
whiptail 1
bullfrog 1
wok 1
go-kart 1
tiger_cat 1
guillotine 1
shower_cap 1
Name: p3, Length: 408, dtype: int64
# Check for duplicated tweet_ids
sum(df_twitter_counts.tweet_id.duplicated())
0
In order to analyze the data, I followed the criteria found below:
The four main data quality dimensions are:
- Completeness: missing data
- Validity: if the data make sense
- Accuracy: inaccurate data (wrong data can still show up as valid)
- Consistency: standardization
DataFrame: df_twitter
DataFrame: df_img_pred
DataFrame: df_twitter_counts
DataFrame: df_twitter
DataFrame: df_img_pred
DataFrame: df_twitter_counts
Create copies of the originals DaraFrames:
# Create a copy of the DataFrame (df)
twitter_clean = df_twitter.copy()
# Create a copy of twitter_counts_df
twitter_counts_clean = df_twitter_counts.copy()
# Create a copy of img_pred.
img_pred_clean = df_img_pred.copy()
Define
In order to be able to analyze the text to get names, breed and rating information, I need to display the full text. I will do it by applying the code line that can be found in the code cell below back where I gathered the Twitter Archive.
Code
# This will set the no truncate for pandas
# pd.set_option('display.max_colwidth', -1)
Test
twitter_clean.text
0 This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU
1 This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV
2 This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB
3 This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ
4 This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f
5 Here we have a majestic great white breaching off South Africa's coast. Absolutely h*ckin breathtaking. 13/10 (IG: tucker_marlo) #BarkWeek https://t.co/kQ04fDDRmh
6 Meet Jax. He enjoys ice cream so much he gets nervous around it. 13/10 help Jax enjoy more things by clicking below\n\nhttps://t.co/Zr4hWfAs1H https://t.co/tVJBRMnhxl
7 When you watch your owner call another dog a good boy but then they turn back to you and say you're a great boy. 13/10 https://t.co/v0nONBcwxq
8 This is Zoey. She doesn't want to be one of the scary sharks. Just wants to be a snuggly pettable boatpet. 13/10 #BarkWeek https://t.co/9TwLuAGH0b
9 This is Cassie. She is a college pup. Studying international doggo communication and stick theory. 14/10 so elegant much sophisticate https://t.co/t1bfwz5S2A
10 This is Koda. He is a South Australian deckshark. Deceptively deadly. Frighteningly majestic. 13/10 would risk a petting #BarkWeek https://t.co/dVPW0B0Mme
11 This is Bruno. He is a service shark. Only gets out of the water to assist you. 13/10 terrifyingly good boy https://t.co/u1XPQMl29g
12 Here's a puppo that seems to be on the fence about something haha no but seriously someone help her. 13/10 https://t.co/BxvuXk0UCm
13 This is Ted. He does his best. Sometimes that's not enough. But it's ok. 12/10 would assist https://t.co/f8dEDcrKSR
14 This is Stuart. He's sporting his favorite fanny pack. Secretly filled with bones only. 13/10 puppared puppo #BarkWeek https://t.co/y70o6h3isq
15 This is Oliver. You're witnessing one of his many brutal attacks. Seems to be playing with his victim. 13/10 fr*ckin frightening #BarkWeek https://t.co/WpHvrQedPb
16 This is Jim. He found a fren. Taught him how to sit like the good boys. 12/10 for both https://t.co/chxruIOUJN
17 This is Zeke. He has a new stick. Very proud of it. Would like you to throw it for him without taking it. 13/10 would do my best https://t.co/HTQ77yNQ5K
18 This is Ralphus. He's powering up. Attempting maximum borkdrive. 13/10 inspirational af https://t.co/YnYAFCTTiK
19 RT @dog_rates: This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX
20 This is Gerald. He was just told he didn't get the job he interviewed for. A h*ckin injustice. 12/10 didn't want the job anyway https://t.co/DK7iDPfuRX
21 This is Jeffrey. He has a monopoly on the pool noodles. Currently running a 'boop for two' midweek sale. 13/10 h*ckin strategic https://t.co/PhrUk20Q64
22 I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba
23 This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX
24 You may not have known you needed to see this today. 13/10 please enjoy (IG: emmylouroo) https://t.co/WZqNqygEyV
25 This... is a Jubilant Antarctic House Bear. We only rate dogs. Please only send dogs. Thank you... 12/10 would suffocate in floof https://t.co/4Ad1jzJSdp
26 This is Maya. She's very shy. Rarely leaves her cup. 13/10 would find her an environment to thrive in https://t.co/I6oNy0CgiT
27 This is Mingus. He's a wonderful father to his smol pup. Confirmed 13/10, but he needs your help\n\nhttps://t.co/bVi0Yr4Cff https://t.co/ISvKOSkd5b
28 This is Derek. He's late for a dog meeting. 13/10 pet...al to the metal https://t.co/BCoWue0abA
29 This is Roscoe. Another pupper fallen victim to spontaneous tongue ejections. Get the BlepiPen immediate. 12/10 deep breaths Roscoe https://t.co/RGE08MIJox
...
2326 This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t
2327 This is a southern Vesuvius bumblegruff. Can drive a truck (wow). Made friends with 5 other nifty dogs (neat). 7/10 https://t.co/LopTBkKa8h
2328 Oh goodness. A super rare northeast Qdoba kangaroo mix. Massive feet. No pouch (disappointing). Seems alert. 9/10 https://t.co/Dc7b0E8qFE
2329 Those are sunglasses and a jean jacket. 11/10 dog cool af https://t.co/uHXrPkUEyl
2330 Unique dog here. Very small. Lives in container of Frosted Flakes (?). Short legs. Must be rare 6/10 would still pet https://t.co/XMD9CwjEnM
2331 Here we have a mixed Asiago from the Galápagos Islands. Only one ear working. Big fan of marijuana carpet. 8/10 https://t.co/tltQ5w9aUO
2332 Look at this jokester thinking seat belt laws don't apply to him. Great tongue tho 10/10 https://t.co/VFKG1vxGjB
2333 This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL
2334 This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0
2335 This is an Albanian 3 1/2 legged Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv
2336 Can take selfies 11/10 https://t.co/ws2AMaNwPW
2337 Very concerned about fellow dog trapped in computer. 10/10 https://t.co/0yxApIikpk
2338 Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX
2339 Oh my. Here you are seeing an Adobe Setter giving birth to twins!!! The world is an amazing place. 11/10 https://t.co/11LvqN4WLq
2340 Can stand on stump for what seems like a while. Built that birdhouse? Impressive. Made friends with a squirrel. 8/10 https://t.co/Ri4nMTLq5C
2341 This appears to be a Mongolian Presbyterian mix. Very tired. Tongue slip confirmed. 9/10 would lie down with https://t.co/mnioXo3IfP
2342 Here we have a well-established sunblockerspaniel. Lost his other flip-flop. 6/10 not very waterproof https://t.co/3RU6x0vHB7
2343 Let's hope this flight isn't Malaysian (lol). What a dog! Almost completely camouflaged. 10/10 I trust this pilot https://t.co/Yk6GHE9tOY
2344 Here we have a northern speckled Rhododendron. Much sass. Gives 0 fucks. Good tongue. 9/10 would caress sensually https://t.co/ZoL8kq2XFx
2345 This is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehK
2346 Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p
2347 My oh my. This is a rare blond Canadian terrier on wheels. Only $8.98. Rather docile. 9/10 very rare https://t.co/yWBqbrzy8O
2348 Here is a Siberian heavily armored polar bear mix. Strong owner. 10/10 I would do unspeakable things to pet this dog https://t.co/rdivxLiqEt
2349 This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc
2350 This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe
2351 Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq
2352 This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx
2353 Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR
2354 This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI
2355 Here we have a Japanese Irish Setter. Lost eye in Vietnam (?). Big fan of relaxing on stair. 8/10 would pet https://t.co/BLDqew2Ijj
Name: text, Length: 2356, dtype: object
Define
Delete retweets and replies in df_twitter table filtering the NaN of retweeted_status_user_id in order to keep only the original tweets.
Code
# Delete retweets
twitter_clean= twitter_clean[twitter_clean.retweeted_status_id.isnull()];
# Delete replies
twitter_clean= twitter_clean[twitter_clean.in_reply_to_status_id.isnull()];
Test
# Check there are no retweets and replies
sum(~ ((twitter_clean.retweeted_status_id.isnull())&(twitter_clean.in_reply_to_status_id.isnull())))
0
Define
I am droping the columns that I will not be using in my analysis from df_twitter dataframe: 'in_reply_to_status_id', 'in_reply_to_user_id', 'source', 'retweeted_status_id','retweeted_status_user_id', 'retweeted_status_timestamp', and 'expanded_urls'
Code
# Drop columns I do not need:
twitter_clean = twitter_clean.drop(['in_reply_to_status_id','in_reply_to_user_id','source',
'retweeted_status_id','retweeted_status_user_id',
'retweeted_status_timestamp'], axis=1)
Test
# Check the columns have been deleted
twitter_clean.columns
Index(['tweet_id', 'timestamp', 'text', 'expanded_urls', 'rating_numerator',
'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo'],
dtype='object')
Define
To correct invalid names (having filtered by names starting with lowercase), in some tweet's, the real name appears after phrases such as: 'named', 'That is' and 'Name is'. I am going to use the if .. elif .. else statement to try extracting the dog's names from the text column and in case it is not possible, return NaN.
Code
# Create a new column 'text_split'
twitter_clean['text_split_words'] = twitter_clean['text'].str.split() #Split a string into a list where each word is a list item
corrected_names = []
# Write lambda function
def extract_names(case):
# 'named'
if 'named' in case['text'] and re.match(r'[A-Z].*', case['text_split_words'][(case['text_split_words'].index('named') + 1)]):
corrected_names.append(case['text_split_words'][(case['text_split_words'].index('named') + 1)])
# [A-Z].* --> Match any uppercase ASCII letter
# 'That is'
elif case['text'].startswith('That is ') and re.match(r'[A-Z].*', case['text_split_words'][2]):
corrected_names.append(case['text_split_words'][2].strip('.').strip(','))
# 'name is'
elif case['text'].startswith('name is ') and re.match(r'[A-Z].*', case['text_split_words'][2]):
corrected_names.append(case['text_split_words'][2].strip('.').strip(','))
# 'Here we have'
elif case['text'].startswith('Here we have ') and re.match(r'[A-Z].*', case['text_split_words'][3]):
corrected_names.append(case['text_split_words'][3].strip('.').strip(','))
# 'Say hello to'
elif case['text'].startswith('Say hello to ') and re.match(r'[A-Z].*', case['text_split_words'][3]):
corrected_names.append(case['text_split_words'][3].strip('.').strip(','))
# 'Meet'
elif case['text'].startswith('Meet ') and re.match(r'[A-Z].*', case['text_split_words'][1]):
corrected_names.append(case['text_split_words'][1].strip('.').strip(','))
# 'This is'
elif case['text'].startswith('This is ') and re.match(r'[A-Z].*', case['text_split_words'][2]):
corrected_names.append(case['text_split_words'][2].strip('.').strip(','))
else:
corrected_names.append('None')
# Source https://github.com/sanjeevai/Wrangle_and_Analyze_data/blob/master/wrangle_act.ipynb
# apply the written function
twitter_clean.apply(extract_names, axis=1)
twitter_clean['corrected_names'] = corrected_names
# Eliminate the drop at the end of the names
twitter_clean.corrected_names.replace('.', '',inplace=True)
# delete the old name column and the tex split column
twitter_clean.drop(['name'], axis=1, inplace=True)
twitter_clean.drop(['text_split_words'], axis=1, inplace=True)
Test
# Check if there are any names starting with lowercase
len(twitter_clean.loc[(twitter_clean['corrected_names'].str.islower())])
0
# Check if the number of 'None' names has deceased (before cleaning: None 745)
twitter_clean.corrected_names.value_counts()
None 687
Lucy 11
Charlie 11
Oliver 10
Cooper 10
Tucker 9
Penny 9
Lola 8
Winston 8
Sadie 8
Daisy 7
Stanley 6
Koda 6
Oscar 6
Bo 6
Bella 6
Bailey 6
Toby 6
Jax 6
Chester 5
Louis 5
Milo 5
Bentley 5
Buddy 5
Scout 5
Leo 5
Rusty 5
Chip 4
Dave 4
Finn 4
..
Kona 1
Berta. 1
Evy 1
Lillie 1
Alfredo. 1
Brian 1
Boots 1
Atlas 1
Odin 1
Lucky 1
Beckham 1
Noah 1
Schnozz 1
Brandy 1
Walker 1
Huck 1
Klein 1
Brooks 1
Brady 1
Tuck 1
Alphred. 1
Iggy 1
Carper 1
Chuk. 1
Liam 1
Bode 1
Dallas 1
Zooey 1
Blanket 1
Livvie 1
Name: corrected_names, Length: 956, dtype: int64
Define
Convert tweet_id to string and timestamp to datetime data type. (I found more datatype errors but they have been corrected in other cleaning steps)
Code
# Change tweet_id type to str from tables and df_twitter df_img_pred
twitter_clean.tweet_id = twitter_clean.tweet_id.astype(str)
img_pred_clean.tweet_id = img_pred_clean.tweet_id.astype(str)
# Change timestamp type to datetime
twitter_clean.timestamp = pd.to_datetime(df_twitter.timestamp)
Test
twitter_clean.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 2097 entries, 0 to 2355 Data columns (total 11 columns): tweet_id 2097 non-null object timestamp 2097 non-null datetime64[ns] text 2097 non-null object expanded_urls 2094 non-null object rating_numerator 2097 non-null int64 rating_denominator 2097 non-null int64 doggo 2097 non-null object floofer 2097 non-null object pupper 2097 non-null object puppo 2097 non-null object corrected_names 2097 non-null object dtypes: datetime64[ns](1), int64(2), object(8) memory usage: 196.6+ KB
img_pred_clean.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2075 entries, 0 to 2074 Data columns (total 12 columns): tweet_id 2075 non-null object jpg_url 2075 non-null object img_num 2075 non-null int64 p1 2075 non-null object p1_conf 2075 non-null float64 p1_dog 2075 non-null bool p2 2075 non-null object p2_conf 2075 non-null float64 p2_dog 2075 non-null bool p3 2075 non-null object p3_conf 2075 non-null float64 p3_dog 2075 non-null bool dtypes: bool(3), float64(3), int64(1), object(5) memory usage: 152.1+ KB
Define
While assesing I detected tweets that were rating two dogs, I am going to delete those as I only want to investigate tweets rating one dog. I am also going to correct the dog stages for those tweets with two stages but only rating one dog.
Code
# Delete tweets rating two dogs
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="808106460588765185"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="802265048156610565"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="781308096455073793"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="775898661951791106"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="770093767776997377"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="759793422261743616"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="741067306818797568"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="733109485275860992"].index, inplace=True)
# Correct dog stages
twitter_clean.loc[twitter_clean['tweet_id']=="854010172552949760",'doggo'] = ''
twitter_clean.loc[twitter_clean['tweet_id']=="855851453814013952",'doggo'] = ''
twitter_clean.loc[twitter_clean['tweet_id']=="817777686764523521",'doggo'] = ''
twitter_clean.loc[twitter_clean['tweet_id']=="801115127852503040",'pupper'] = ''
twitter_clean.loc[twitter_clean['tweet_id']=="785639753186217984",'pupper'] = ''
twitter_clean.loc[twitter_clean['tweet_id']=="751583847268179968",'pupper'] = ''
Test
twitter_clean[((twitter_clean.doggo != "None") & (twitter_clean.floofer != "None"))&
((twitter_clean.doggo != '') & (twitter_clean.floofer != ''))]
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | doggo | floofer | pupper | puppo | corrected_names |
|---|
twitter_clean[((twitter_clean.doggo != "None") & (twitter_clean.pupper != "None"))&
((twitter_clean.doggo != '') & (twitter_clean.pupper != ''))]
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | doggo | floofer | pupper | puppo | corrected_names |
|---|
twitter_clean[((twitter_clean.doggo != "None") & (twitter_clean.puppo != "None"))&
((twitter_clean.doggo != '') & (twitter_clean.puppo != ''))]
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | doggo | floofer | pupper | puppo | corrected_names |
|---|
Define
The missings for name column in archive table (twitter_clean) are represented as "None" instead of Null, therefore I will replace this missing data from None to NaN using .replace function.
The missings for doggo, floofer, pupper, and puppo columns in archive table are represented as "None". I will replace those "None" for '' to be able to concatenate the four columns (in further cleaning steps) getting one with the dog's stage.
Code
# Replace 'None' values for NaN
twitter_clean['corrected_names'].replace('None', np.nan, inplace=True)
twitter_clean['doggo'].replace('None', '', inplace=True)
twitter_clean['floofer'].replace('None', '', inplace=True)
twitter_clean['pupper'].replace('None', '', inplace=True)
twitter_clean['puppo'].replace('None', '', inplace=True)
Test
# Check there are not 'None' values in the columns
print("'None' values in name column:", twitter_clean['corrected_names'].str.contains('None').sum())
print("'None' values in doggo column:", twitter_clean['doggo'].str.contains('None').sum())
print("'None' values in floofer column:", twitter_clean['floofer'].str.contains('None').sum())
print("'None' values in pupper column:", twitter_clean['pupper'].str.contains('None').sum())
print("'None' values in puppo column:", twitter_clean['puppo'].str.contains('None').sum())
'None' values in name column: 0 'None' values in doggo column: 0 'None' values in floofer column: 0 'None' values in pupper column: 0 'None' values in puppo column: 0
Define
Tansform the 4 columns into one by concatenating them and obtaining the dog_stage column with the stege of each dog.
Code
twitter_clean.groupby(["doggo", "floofer", "pupper", "puppo"]).size().reset_index().rename(columns={0: "count"})
| doggo | floofer | pupper | puppo | count | |
|---|---|---|---|---|---|
| 0 | 1761 | ||||
| 1 | puppo | 24 | |||
| 2 | pupper | 222 | |||
| 3 | floofer | 10 | |||
| 4 | doggo | 75 |
# Concatenate the dog stage columns into one single column (dog_stage)
twitter_clean['dog_stage'] = twitter_clean.doggo + twitter_clean.floofer + twitter_clean.pupper + twitter_clean.puppo
# Set the empty dog_stage values as NaN
twitter_clean.loc[twitter_clean.dog_stage == '', 'dog_stage'] = np.nan
# Delete the useless columns ("doggo", "floofer", "pupper", "puppo")
twitter_clean.drop(['doggo', 'floofer', 'pupper', 'puppo'], axis=1, inplace=True)
Test
twitter_clean
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | corrected_names | dog_stage | |
|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | 2017-08-01 16:23:56 | This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU | https://twitter.com/dog_rates/status/892420643555336193/photo/1 | 13 | 10 | Phineas | NaN |
| 1 | 892177421306343426 | 2017-08-01 00:17:27 | This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV | https://twitter.com/dog_rates/status/892177421306343426/photo/1 | 13 | 10 | Tilly | NaN |
| 2 | 891815181378084864 | 2017-07-31 00:18:03 | This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB | https://twitter.com/dog_rates/status/891815181378084864/photo/1 | 12 | 10 | Archie | NaN |
| 3 | 891689557279858688 | 2017-07-30 15:58:51 | This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ | https://twitter.com/dog_rates/status/891689557279858688/photo/1 | 13 | 10 | Darla | NaN |
| 4 | 891327558926688256 | 2017-07-29 16:00:24 | This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f | https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1 | 12 | 10 | Franklin | NaN |
| 5 | 891087950875897856 | 2017-07-29 00:08:17 | Here we have a majestic great white breaching off South Africa's coast. Absolutely h*ckin breathtaking. 13/10 (IG: tucker_marlo) #BarkWeek https://t.co/kQ04fDDRmh | https://twitter.com/dog_rates/status/891087950875897856/photo/1 | 13 | 10 | NaN | NaN |
| 6 | 890971913173991426 | 2017-07-28 16:27:12 | Meet Jax. He enjoys ice cream so much he gets nervous around it. 13/10 help Jax enjoy more things by clicking below\n\nhttps://t.co/Zr4hWfAs1H https://t.co/tVJBRMnhxl | https://gofundme.com/ydvmve-surgery-for-jax,https://twitter.com/dog_rates/status/890971913173991426/photo/1 | 13 | 10 | Jax | NaN |
| 7 | 890729181411237888 | 2017-07-28 00:22:40 | When you watch your owner call another dog a good boy but then they turn back to you and say you're a great boy. 13/10 https://t.co/v0nONBcwxq | https://twitter.com/dog_rates/status/890729181411237888/photo/1,https://twitter.com/dog_rates/status/890729181411237888/photo/1 | 13 | 10 | NaN | NaN |
| 8 | 890609185150312448 | 2017-07-27 16:25:51 | This is Zoey. She doesn't want to be one of the scary sharks. Just wants to be a snuggly pettable boatpet. 13/10 #BarkWeek https://t.co/9TwLuAGH0b | https://twitter.com/dog_rates/status/890609185150312448/photo/1 | 13 | 10 | Zoey | NaN |
| 9 | 890240255349198849 | 2017-07-26 15:59:51 | This is Cassie. She is a college pup. Studying international doggo communication and stick theory. 14/10 so elegant much sophisticate https://t.co/t1bfwz5S2A | https://twitter.com/dog_rates/status/890240255349198849/photo/1 | 14 | 10 | Cassie | doggo |
| 10 | 890006608113172480 | 2017-07-26 00:31:25 | This is Koda. He is a South Australian deckshark. Deceptively deadly. Frighteningly majestic. 13/10 would risk a petting #BarkWeek https://t.co/dVPW0B0Mme | https://twitter.com/dog_rates/status/890006608113172480/photo/1,https://twitter.com/dog_rates/status/890006608113172480/photo/1 | 13 | 10 | Koda | NaN |
| 11 | 889880896479866881 | 2017-07-25 16:11:53 | This is Bruno. He is a service shark. Only gets out of the water to assist you. 13/10 terrifyingly good boy https://t.co/u1XPQMl29g | https://twitter.com/dog_rates/status/889880896479866881/photo/1 | 13 | 10 | Bruno | NaN |
| 12 | 889665388333682689 | 2017-07-25 01:55:32 | Here's a puppo that seems to be on the fence about something haha no but seriously someone help her. 13/10 https://t.co/BxvuXk0UCm | https://twitter.com/dog_rates/status/889665388333682689/photo/1 | 13 | 10 | NaN | puppo |
| 13 | 889638837579907072 | 2017-07-25 00:10:02 | This is Ted. He does his best. Sometimes that's not enough. But it's ok. 12/10 would assist https://t.co/f8dEDcrKSR | https://twitter.com/dog_rates/status/889638837579907072/photo/1,https://twitter.com/dog_rates/status/889638837579907072/photo/1 | 12 | 10 | Ted | NaN |
| 14 | 889531135344209921 | 2017-07-24 17:02:04 | This is Stuart. He's sporting his favorite fanny pack. Secretly filled with bones only. 13/10 puppared puppo #BarkWeek https://t.co/y70o6h3isq | https://twitter.com/dog_rates/status/889531135344209921/photo/1 | 13 | 10 | Stuart | puppo |
| 15 | 889278841981685760 | 2017-07-24 00:19:32 | This is Oliver. You're witnessing one of his many brutal attacks. Seems to be playing with his victim. 13/10 fr*ckin frightening #BarkWeek https://t.co/WpHvrQedPb | https://twitter.com/dog_rates/status/889278841981685760/video/1 | 13 | 10 | Oliver | NaN |
| 16 | 888917238123831296 | 2017-07-23 00:22:39 | This is Jim. He found a fren. Taught him how to sit like the good boys. 12/10 for both https://t.co/chxruIOUJN | https://twitter.com/dog_rates/status/888917238123831296/photo/1 | 12 | 10 | Jim | NaN |
| 17 | 888804989199671297 | 2017-07-22 16:56:37 | This is Zeke. He has a new stick. Very proud of it. Would like you to throw it for him without taking it. 13/10 would do my best https://t.co/HTQ77yNQ5K | https://twitter.com/dog_rates/status/888804989199671297/photo/1,https://twitter.com/dog_rates/status/888804989199671297/photo/1 | 13 | 10 | Zeke | NaN |
| 18 | 888554962724278272 | 2017-07-22 00:23:06 | This is Ralphus. He's powering up. Attempting maximum borkdrive. 13/10 inspirational af https://t.co/YnYAFCTTiK | https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1 | 13 | 10 | Ralphus | NaN |
| 20 | 888078434458587136 | 2017-07-20 16:49:33 | This is Gerald. He was just told he didn't get the job he interviewed for. A h*ckin injustice. 12/10 didn't want the job anyway https://t.co/DK7iDPfuRX | https://twitter.com/dog_rates/status/888078434458587136/photo/1,https://twitter.com/dog_rates/status/888078434458587136/photo/1 | 12 | 10 | Gerald | NaN |
| 21 | 887705289381826560 | 2017-07-19 16:06:48 | This is Jeffrey. He has a monopoly on the pool noodles. Currently running a 'boop for two' midweek sale. 13/10 h*ckin strategic https://t.co/PhrUk20Q64 | https://twitter.com/dog_rates/status/887705289381826560/photo/1 | 13 | 10 | Jeffrey | NaN |
| 22 | 887517139158093824 | 2017-07-19 03:39:09 | I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba | https://twitter.com/dog_rates/status/887517139158093824/video/1 | 14 | 10 | NaN | NaN |
| 23 | 887473957103951883 | 2017-07-19 00:47:34 | This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX | https://twitter.com/dog_rates/status/887473957103951883/photo/1,https://twitter.com/dog_rates/status/887473957103951883/photo/1 | 13 | 10 | Canela | NaN |
| 24 | 887343217045368832 | 2017-07-18 16:08:03 | You may not have known you needed to see this today. 13/10 please enjoy (IG: emmylouroo) https://t.co/WZqNqygEyV | https://twitter.com/dog_rates/status/887343217045368832/video/1 | 13 | 10 | NaN | NaN |
| 25 | 887101392804085760 | 2017-07-18 00:07:08 | This... is a Jubilant Antarctic House Bear. We only rate dogs. Please only send dogs. Thank you... 12/10 would suffocate in floof https://t.co/4Ad1jzJSdp | https://twitter.com/dog_rates/status/887101392804085760/photo/1 | 12 | 10 | NaN | NaN |
| 26 | 886983233522544640 | 2017-07-17 16:17:36 | This is Maya. She's very shy. Rarely leaves her cup. 13/10 would find her an environment to thrive in https://t.co/I6oNy0CgiT | https://twitter.com/dog_rates/status/886983233522544640/photo/1,https://twitter.com/dog_rates/status/886983233522544640/photo/1 | 13 | 10 | Maya | NaN |
| 27 | 886736880519319552 | 2017-07-16 23:58:41 | This is Mingus. He's a wonderful father to his smol pup. Confirmed 13/10, but he needs your help\n\nhttps://t.co/bVi0Yr4Cff https://t.co/ISvKOSkd5b | https://www.gofundme.com/mingusneedsus,https://twitter.com/dog_rates/status/886736880519319552/photo/1,https://twitter.com/dog_rates/status/886736880519319552/photo/1 | 13 | 10 | Mingus | NaN |
| 28 | 886680336477933568 | 2017-07-16 20:14:00 | This is Derek. He's late for a dog meeting. 13/10 pet...al to the metal https://t.co/BCoWue0abA | https://twitter.com/dog_rates/status/886680336477933568/photo/1 | 13 | 10 | Derek | NaN |
| 29 | 886366144734445568 | 2017-07-15 23:25:31 | This is Roscoe. Another pupper fallen victim to spontaneous tongue ejections. Get the BlepiPen immediate. 12/10 deep breaths Roscoe https://t.co/RGE08MIJox | https://twitter.com/dog_rates/status/886366144734445568/photo/1,https://twitter.com/dog_rates/status/886366144734445568/photo/1 | 12 | 10 | Roscoe | pupper |
| 31 | 886258384151887873 | 2017-07-15 16:17:19 | This is Waffles. His doggles are pupside down. Unsure how to fix. 13/10 someone assist Waffles https://t.co/xZDA9Qsq1O | https://twitter.com/dog_rates/status/886258384151887873/photo/1 | 13 | 10 | Waffles | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2326 | 666411507551481857 | 2015-11-17 00:24:19 | This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t | https://twitter.com/dog_rates/status/666411507551481857/photo/1 | 2 | 10 | NaN | NaN |
| 2327 | 666407126856765440 | 2015-11-17 00:06:54 | This is a southern Vesuvius bumblegruff. Can drive a truck (wow). Made friends with 5 other nifty dogs (neat). 7/10 https://t.co/LopTBkKa8h | https://twitter.com/dog_rates/status/666407126856765440/photo/1 | 7 | 10 | NaN | NaN |
| 2328 | 666396247373291520 | 2015-11-16 23:23:41 | Oh goodness. A super rare northeast Qdoba kangaroo mix. Massive feet. No pouch (disappointing). Seems alert. 9/10 https://t.co/Dc7b0E8qFE | https://twitter.com/dog_rates/status/666396247373291520/photo/1 | 9 | 10 | NaN | NaN |
| 2329 | 666373753744588802 | 2015-11-16 21:54:18 | Those are sunglasses and a jean jacket. 11/10 dog cool af https://t.co/uHXrPkUEyl | https://twitter.com/dog_rates/status/666373753744588802/photo/1 | 11 | 10 | NaN | NaN |
| 2330 | 666362758909284353 | 2015-11-16 21:10:36 | Unique dog here. Very small. Lives in container of Frosted Flakes (?). Short legs. Must be rare 6/10 would still pet https://t.co/XMD9CwjEnM | https://twitter.com/dog_rates/status/666362758909284353/photo/1 | 6 | 10 | NaN | NaN |
| 2331 | 666353288456101888 | 2015-11-16 20:32:58 | Here we have a mixed Asiago from the Galápagos Islands. Only one ear working. Big fan of marijuana carpet. 8/10 https://t.co/tltQ5w9aUO | https://twitter.com/dog_rates/status/666353288456101888/photo/1 | 8 | 10 | NaN | NaN |
| 2332 | 666345417576210432 | 2015-11-16 20:01:42 | Look at this jokester thinking seat belt laws don't apply to him. Great tongue tho 10/10 https://t.co/VFKG1vxGjB | https://twitter.com/dog_rates/status/666345417576210432/photo/1 | 10 | 10 | NaN | NaN |
| 2333 | 666337882303524864 | 2015-11-16 19:31:45 | This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL | https://twitter.com/dog_rates/status/666337882303524864/photo/1 | 9 | 10 | NaN | NaN |
| 2334 | 666293911632134144 | 2015-11-16 16:37:02 | This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0 | https://twitter.com/dog_rates/status/666293911632134144/photo/1 | 3 | 10 | NaN | NaN |
| 2335 | 666287406224695296 | 2015-11-16 16:11:11 | This is an Albanian 3 1/2 legged Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv | https://twitter.com/dog_rates/status/666287406224695296/photo/1 | 1 | 2 | NaN | NaN |
| 2336 | 666273097616637952 | 2015-11-16 15:14:19 | Can take selfies 11/10 https://t.co/ws2AMaNwPW | https://twitter.com/dog_rates/status/666273097616637952/photo/1 | 11 | 10 | NaN | NaN |
| 2337 | 666268910803644416 | 2015-11-16 14:57:41 | Very concerned about fellow dog trapped in computer. 10/10 https://t.co/0yxApIikpk | https://twitter.com/dog_rates/status/666268910803644416/photo/1 | 10 | 10 | NaN | NaN |
| 2338 | 666104133288665088 | 2015-11-16 04:02:55 | Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX | https://twitter.com/dog_rates/status/666104133288665088/photo/1 | 1 | 10 | NaN | NaN |
| 2339 | 666102155909144576 | 2015-11-16 03:55:04 | Oh my. Here you are seeing an Adobe Setter giving birth to twins!!! The world is an amazing place. 11/10 https://t.co/11LvqN4WLq | https://twitter.com/dog_rates/status/666102155909144576/photo/1 | 11 | 10 | NaN | NaN |
| 2340 | 666099513787052032 | 2015-11-16 03:44:34 | Can stand on stump for what seems like a while. Built that birdhouse? Impressive. Made friends with a squirrel. 8/10 https://t.co/Ri4nMTLq5C | https://twitter.com/dog_rates/status/666099513787052032/photo/1 | 8 | 10 | NaN | NaN |
| 2341 | 666094000022159362 | 2015-11-16 03:22:39 | This appears to be a Mongolian Presbyterian mix. Very tired. Tongue slip confirmed. 9/10 would lie down with https://t.co/mnioXo3IfP | https://twitter.com/dog_rates/status/666094000022159362/photo/1 | 9 | 10 | NaN | NaN |
| 2342 | 666082916733198337 | 2015-11-16 02:38:37 | Here we have a well-established sunblockerspaniel. Lost his other flip-flop. 6/10 not very waterproof https://t.co/3RU6x0vHB7 | https://twitter.com/dog_rates/status/666082916733198337/photo/1 | 6 | 10 | NaN | NaN |
| 2343 | 666073100786774016 | 2015-11-16 01:59:36 | Let's hope this flight isn't Malaysian (lol). What a dog! Almost completely camouflaged. 10/10 I trust this pilot https://t.co/Yk6GHE9tOY | https://twitter.com/dog_rates/status/666073100786774016/photo/1 | 10 | 10 | NaN | NaN |
| 2344 | 666071193221509120 | 2015-11-16 01:52:02 | Here we have a northern speckled Rhododendron. Much sass. Gives 0 fucks. Good tongue. 9/10 would caress sensually https://t.co/ZoL8kq2XFx | https://twitter.com/dog_rates/status/666071193221509120/photo/1 | 9 | 10 | NaN | NaN |
| 2345 | 666063827256086533 | 2015-11-16 01:22:45 | This is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehK | https://twitter.com/dog_rates/status/666063827256086533/photo/1 | 10 | 10 | NaN | NaN |
| 2346 | 666058600524156928 | 2015-11-16 01:01:59 | Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p | https://twitter.com/dog_rates/status/666058600524156928/photo/1 | 8 | 10 | NaN | NaN |
| 2347 | 666057090499244032 | 2015-11-16 00:55:59 | My oh my. This is a rare blond Canadian terrier on wheels. Only $8.98. Rather docile. 9/10 very rare https://t.co/yWBqbrzy8O | https://twitter.com/dog_rates/status/666057090499244032/photo/1 | 9 | 10 | NaN | NaN |
| 2348 | 666055525042405380 | 2015-11-16 00:49:46 | Here is a Siberian heavily armored polar bear mix. Strong owner. 10/10 I would do unspeakable things to pet this dog https://t.co/rdivxLiqEt | https://twitter.com/dog_rates/status/666055525042405380/photo/1 | 10 | 10 | NaN | NaN |
| 2349 | 666051853826850816 | 2015-11-16 00:35:11 | This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc | https://twitter.com/dog_rates/status/666051853826850816/photo/1 | 2 | 10 | NaN | NaN |
| 2350 | 666050758794694657 | 2015-11-16 00:30:50 | This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe | https://twitter.com/dog_rates/status/666050758794694657/photo/1 | 10 | 10 | NaN | NaN |
| 2351 | 666049248165822465 | 2015-11-16 00:24:50 | Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq | https://twitter.com/dog_rates/status/666049248165822465/photo/1 | 5 | 10 | NaN | NaN |
| 2352 | 666044226329800704 | 2015-11-16 00:04:52 | This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx | https://twitter.com/dog_rates/status/666044226329800704/photo/1 | 6 | 10 | NaN | NaN |
| 2353 | 666033412701032449 | 2015-11-15 23:21:54 | Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR | https://twitter.com/dog_rates/status/666033412701032449/photo/1 | 9 | 10 | NaN | NaN |
| 2354 | 666029285002620928 | 2015-11-15 23:05:30 | This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI | https://twitter.com/dog_rates/status/666029285002620928/photo/1 | 7 | 10 | NaN | NaN |
| 2355 | 666020888022790149 | 2015-11-15 22:32:08 | Here we have a Japanese Irish Setter. Lost eye in Vietnam (?). Big fan of relaxing on stair. 8/10 would pet https://t.co/BLDqew2Ijj | https://twitter.com/dog_rates/status/666020888022790149/photo/1 | 8 | 10 | NaN | NaN |
2092 rows × 8 columns
twitter_clean.dog_stage.value_counts()
pupper 222 doggo 75 puppo 24 floofer 10 Name: dog_stage, dtype: int64
Define
twitter_clean[twitter_clean['rating_denominator']!=10]
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | corrected_names | dog_stage | |
|---|---|---|---|---|---|---|---|---|
| 433 | 820690176645140481 | 2017-01-15 17:52:40 | The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd | https://twitter.com/dog_rates/status/820690176645140481/photo/1,https://twitter.com/dog_rates/status/820690176645140481/photo/1,https://twitter.com/dog_rates/status/820690176645140481/photo/1 | 84 | 70 | NaN | NaN |
| 516 | 810984652412424192 | 2016-12-19 23:06:23 | Meet Sam. She smiles 24/7 & secretly aspires to be a reindeer. \nKeep Sam smiling by clicking and sharing this link:\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx | https://www.gofundme.com/sams-smile,https://twitter.com/dog_rates/status/810984652412424192/photo/1 | 24 | 7 | Sam | NaN |
| 902 | 758467244762497024 | 2016-07-28 01:00:57 | Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE | https://twitter.com/dog_rates/status/758467244762497024/video/1 | 165 | 150 | NaN | NaN |
| 1068 | 740373189193256964 | 2016-06-08 02:41:38 | After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ | https://twitter.com/dog_rates/status/740373189193256964/photo/1,https://twitter.com/dog_rates/status/740373189193256964/photo/1,https://twitter.com/dog_rates/status/740373189193256964/photo/1,https://twitter.com/dog_rates/status/740373189193256964/photo/1 | 9 | 11 | NaN | NaN |
| 1120 | 731156023742988288 | 2016-05-13 16:15:54 | Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv | https://twitter.com/dog_rates/status/731156023742988288/photo/1 | 204 | 170 | NaN | NaN |
| 1165 | 722974582966214656 | 2016-04-21 02:25:47 | Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a | https://twitter.com/dog_rates/status/722974582966214656/photo/1 | 4 | 20 | NaN | NaN |
| 1202 | 716439118184652801 | 2016-04-03 01:36:11 | This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq | https://twitter.com/dog_rates/status/716439118184652801/photo/1 | 50 | 50 | Bluebert | NaN |
| 1228 | 713900603437621249 | 2016-03-27 01:29:02 | Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1 | https://twitter.com/dog_rates/status/713900603437621249/photo/1 | 99 | 90 | NaN | NaN |
| 1254 | 710658690886586372 | 2016-03-18 02:46:49 | Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12 | https://twitter.com/dog_rates/status/710658690886586372/photo/1 | 80 | 80 | NaN | NaN |
| 1274 | 709198395643068416 | 2016-03-14 02:04:08 | From left to right:\nCletus, Jerome, Alejandro, Burp, & Titson\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK | https://twitter.com/dog_rates/status/709198395643068416/photo/1 | 45 | 50 | NaN | NaN |
| 1351 | 704054845121142784 | 2016-02-28 21:25:30 | Here is a whole flock of puppers. 60/50 I'll take the lot https://t.co/9dpcw6MdWa | https://twitter.com/dog_rates/status/704054845121142784/photo/1 | 60 | 50 | NaN | NaN |
| 1433 | 697463031882764288 | 2016-02-10 16:51:59 | Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ | https://twitter.com/dog_rates/status/697463031882764288/photo/1 | 44 | 40 | NaN | NaN |
| 1635 | 684222868335505415 | 2016-01-05 04:00:18 | Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55 | https://twitter.com/dog_rates/status/684222868335505415/photo/1 | 121 | 110 | NaN | NaN |
| 1662 | 682962037429899265 | 2016-01-01 16:30:13 | This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5 | https://twitter.com/dog_rates/status/682962037429899265/photo/1 | 7 | 11 | Darrel | NaN |
| 1779 | 677716515794329600 | 2015-12-18 05:06:23 | IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq | https://twitter.com/dog_rates/status/677716515794329600/photo/1 | 144 | 120 | NaN | NaN |
| 1843 | 675853064436391936 | 2015-12-13 01:41:41 | Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw | https://twitter.com/dog_rates/status/675853064436391936/photo/1,https://twitter.com/dog_rates/status/675853064436391936/photo/1 | 88 | 80 | NaN | NaN |
| 2335 | 666287406224695296 | 2015-11-16 16:11:11 | This is an Albanian 3 1/2 legged Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv | https://twitter.com/dog_rates/status/666287406224695296/photo/1 | 1 | 2 | NaN | NaN |
By visually inspecting tweets with a rating denominator other than the standard metric (10), I can determine, using the text and the urls, the actual denominator (as well as the numerator).
As mentioned above, I just want to analyze tweets that rate one dog, therefore I will remove rows for which the tweet rates more than one dog.
| tweet_id | Corrected rating_denominator |
|---|---|
| 758467244762497024 | several dogs--> Drop row |
| 820690176645140481 | More than 1 dog--> Drop row |
| 810984652412424192 | NaN --> Drop row |
| 740373189193256964 | 10 |
| 731156023742988288 | 2 dogs --> Drop row |
| 722974582966214656 | 2 dogs --> Drop row |
| 716439118184652801 | 10 |
| 713900603437621249 | several dogs--> Drop row |
| 710658690886586372 | several dogs--> Drop row |
| 709198395643068416 | several dogs--> Drop row |
| 704054845121142784 | several dogs--> Drop row |
| 697463031882764288 | several dogs--> Drop row |
| 684222868335505415 | several dogs--> Drop row |
| 682962037429899265 | 10 |
| 677716515794329600 | several dogs--> Drop row |
| 675853064436391936 | several dogs--> Drop row |
| 666287406224695296 | 10 |
| tweet_id | Corrected rating_numerator |
|---|---|
| 740373189193256964 | 14 |
| 716439118184652801 | 11 |
| 682962037429899265 | 10 |
| 666287406224695296 | 9 |
Code
# Delete tweets rating two dogs
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="820690176645140481"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="758467244762497024"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="810984652412424192"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="731156023742988288"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="722974582966214656"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="713900603437621249"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="710658690886586372"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="709198395643068416"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="704054845121142784"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="697463031882764288"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="684222868335505415"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="677716515794329600"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="675853064436391936"].index, inplace=True)
# Correct denominators
twitter_clean.loc[twitter_clean['tweet_id']=="740373189193256964",'rating_denominator'] = 10.0
twitter_clean.loc[twitter_clean['tweet_id']=="716439118184652801",'rating_denominator'] = 10.0
twitter_clean.loc[twitter_clean['tweet_id']=="682962037429899265",'rating_denominator'] = 10.0
twitter_clean.loc[twitter_clean['tweet_id']=="666287406224695296",'rating_denominator'] = 10.0
# Correct numerators
twitter_clean.loc[twitter_clean['tweet_id']=="740373189193256964",'rating_numerator'] = 14.0
twitter_clean.loc[twitter_clean['tweet_id']=="716439118184652801",'rating_numerator'] = 11.0
twitter_clean.loc[twitter_clean['tweet_id']=="682962037429899265",'rating_numerator'] = 10.0
twitter_clean.loc[twitter_clean['tweet_id']=="666287406224695296",'rating_numerator'] = 9.0
Test
twitter_clean[twitter_clean['rating_denominator']!=10]
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | corrected_names | dog_stage |
|---|
twitter_clean.rating_denominator.value_counts()
10.0 2079 Name: rating_denominator, dtype: int64
Define
In WeRateDogs it is accepted that the numerators can be greater than 10, however I will drop those numerators greater than 20 since they do not represent the norm and can be considered as spikes that would skew the sample. I will also correct the values misinterpreted by the algorithm as they are a decimal number.
Code
# Display the numerator values higher than 20
twitter_clean[twitter_clean['rating_numerator']>20.0]
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | corrected_names | dog_stage | |
|---|---|---|---|---|---|---|---|---|
| 695 | 786709082849828864 | 2016-10-13 23:23:56 | This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS | https://twitter.com/dog_rates/status/786709082849828864/photo/1 | 75.0 | 10.0 | Logan | NaN |
| 763 | 778027034220126208 | 2016-09-20 00:24:34 | This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq | https://twitter.com/dog_rates/status/778027034220126208/photo/1 | 27.0 | 10.0 | Sophie | pupper |
| 979 | 749981277374128128 | 2016-07-04 15:00:45 | This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh | https://twitter.com/dog_rates/status/749981277374128128/photo/1 | 1776.0 | 10.0 | Atticus | NaN |
| 1712 | 680494726643068929 | 2015-12-25 21:06:00 | Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD | https://twitter.com/dog_rates/status/680494726643068929/photo/1 | 26.0 | 10.0 | NaN | NaN |
| 2074 | 670842764863651840 | 2015-11-29 05:52:33 | After so many requests... here you go.\n\nGood dogg. 420/10 https://t.co/yfAAo1gdeY | https://twitter.com/dog_rates/status/670842764863651840/photo/1 | 420.0 | 10.0 | NaN | NaN |
# Drop numerators greater than 20
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="749981277374128128"].index, inplace=True)
twitter_clean.drop(twitter_clean[twitter_clean['tweet_id']=="670842764863651840"].index, inplace=True)
# View tweets with decimals in rating in 'text' column
twitter_clean[twitter_clean.text.str.contains(r"(\d+\.\d*\/\d+)")]
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | corrected_names | dog_stage | |
|---|---|---|---|---|---|---|---|---|
| 45 | 883482846933004288 | 2017-07-08 00:28:19 | This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948 | https://twitter.com/dog_rates/status/883482846933004288/photo/1,https://twitter.com/dog_rates/status/883482846933004288/photo/1 | 5.0 | 10.0 | Bella | NaN |
| 695 | 786709082849828864 | 2016-10-13 23:23:56 | This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS | https://twitter.com/dog_rates/status/786709082849828864/photo/1 | 75.0 | 10.0 | Logan | NaN |
| 763 | 778027034220126208 | 2016-09-20 00:24:34 | This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq | https://twitter.com/dog_rates/status/778027034220126208/photo/1 | 27.0 | 10.0 | Sophie | pupper |
| 1712 | 680494726643068929 | 2015-12-25 21:06:00 | Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD | https://twitter.com/dog_rates/status/680494726643068929/photo/1 | 26.0 | 10.0 | NaN | NaN |
# Correct numerators with the comments in the text column
twitter_clean.loc[twitter_clean['tweet_id'] == "883482846933004288",'rating_numerator'] = 13.5
twitter_clean.loc[twitter_clean['tweet_id'] == "786709082849828864",'rating_numerator'] = 9.75
twitter_clean.loc[twitter_clean['tweet_id'] == "778027034220126208",'rating_numerator'] = 11.27
twitter_clean.loc[twitter_clean['tweet_id'] == "680494726643068929",'rating_numerator'] = 11.26
Test
# Check there are not numerators greater than 20
twitter_clean[twitter_clean['rating_numerator']>20.0]
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | corrected_names | dog_stage |
|---|
# check that numerators with decimals are corrected and match the rating in the text
twitter_clean[twitter_clean.text.str.contains(r"(\d+\.\d*\/\d+)")].loc[:,['text','rating_numerator','rating_denominator']]
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.
| text | rating_numerator | rating_denominator | |
|---|---|---|---|
| 45 | This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948 | 13.50 | 10.0 |
| 695 | This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS | 9.75 | 10.0 |
| 763 | This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq | 11.27 | 10.0 |
| 1712 | Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD | 11.26 | 10.0 |
Define
Delete duplicated urls from img_pred_clean
Code
# Drop duplicated urls
img_pred_clean.drop_duplicates(['jpg_url'],keep='first',inplace=True)
Test
sum(img_pred_clean['jpg_url'].duplicated())
0
Define
Create a new DataFrame that contains each stub name as a variable, with new index (i, j) using the pandas.wide_to_long function. Each row of these wide variables are assumed to be uniquely identified by i ('tweet_id', 'jpg_url', 'img_num')
Source: pandas.wide_to_long
Code
# modify df_img_pred DataFrame column naming to be able to apply the pandas wide_to_long function
img_pred_clean.rename(columns={"p1": "pred_1", "p1_conf": "conf_1", "p1_dog": "dog_1",
"p2": "pred_2", "p2_conf": "conf_2", "p2_dog": "dog_2",
"p3": "pred_3", "p3_conf": "conf_3", "p3_dog": "dog_3"},inplace=True)
# Columns conversion wide to long (Multiple column melt)
img_pred_clean=pd.wide_to_long(img_pred_clean, stubnames=['pred', 'conf', 'dog'],
i=['tweet_id', 'jpg_url', 'img_num'], j='prediction_number', sep='_')\
.reset_index()
# Change prediction_number type to str
img_pred_clean.prediction_number = img_pred_clean.prediction_number.astype(str)
Test
# Visually check our new DataFrame layout
img_pred_clean
| tweet_id | jpg_url | img_num | prediction_number | pred | conf | dog | |
|---|---|---|---|---|---|---|---|
| 0 | 666020888022790149 | https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg | 1 | 1 | Welsh_springer_spaniel | 0.465074 | True |
| 1 | 666020888022790149 | https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg | 1 | 2 | collie | 0.156665 | True |
| 2 | 666020888022790149 | https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg | 1 | 3 | Shetland_sheepdog | 0.061428 | True |
| 3 | 666029285002620928 | https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg | 1 | 1 | redbone | 0.506826 | True |
| 4 | 666029285002620928 | https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg | 1 | 2 | miniature_pinscher | 0.074192 | True |
| 5 | 666029285002620928 | https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg | 1 | 3 | Rhodesian_ridgeback | 0.072010 | True |
| 6 | 666033412701032449 | https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg | 1 | 1 | German_shepherd | 0.596461 | True |
| 7 | 666033412701032449 | https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg | 1 | 2 | malinois | 0.138584 | True |
| 8 | 666033412701032449 | https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg | 1 | 3 | bloodhound | 0.116197 | True |
| 9 | 666044226329800704 | https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg | 1 | 1 | Rhodesian_ridgeback | 0.408143 | True |
| 10 | 666044226329800704 | https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg | 1 | 2 | redbone | 0.360687 | True |
| 11 | 666044226329800704 | https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg | 1 | 3 | miniature_pinscher | 0.222752 | True |
| 12 | 666049248165822465 | https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg | 1 | 1 | miniature_pinscher | 0.560311 | True |
| 13 | 666049248165822465 | https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg | 1 | 2 | Rottweiler | 0.243682 | True |
| 14 | 666049248165822465 | https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg | 1 | 3 | Doberman | 0.154629 | True |
| 15 | 666050758794694657 | https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg | 1 | 1 | Bernese_mountain_dog | 0.651137 | True |
| 16 | 666050758794694657 | https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg | 1 | 2 | English_springer | 0.263788 | True |
| 17 | 666050758794694657 | https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg | 1 | 3 | Greater_Swiss_Mountain_dog | 0.016199 | True |
| 18 | 666051853826850816 | https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg | 1 | 1 | box_turtle | 0.933012 | False |
| 19 | 666051853826850816 | https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg | 1 | 2 | mud_turtle | 0.045885 | False |
| 20 | 666051853826850816 | https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg | 1 | 3 | terrapin | 0.017885 | False |
| 21 | 666055525042405380 | https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg | 1 | 1 | chow | 0.692517 | True |
| 22 | 666055525042405380 | https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg | 1 | 2 | Tibetan_mastiff | 0.058279 | True |
| 23 | 666055525042405380 | https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg | 1 | 3 | fur_coat | 0.054449 | False |
| 24 | 666057090499244032 | https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg | 1 | 1 | shopping_cart | 0.962465 | False |
| 25 | 666057090499244032 | https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg | 1 | 2 | shopping_basket | 0.014594 | False |
| 26 | 666057090499244032 | https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg | 1 | 3 | golden_retriever | 0.007959 | True |
| 27 | 666058600524156928 | https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg | 1 | 1 | miniature_poodle | 0.201493 | True |
| 28 | 666058600524156928 | https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg | 1 | 2 | komondor | 0.192305 | True |
| 29 | 666058600524156928 | https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg | 1 | 3 | soft-coated_wheaten_terrier | 0.082086 | True |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 5997 | 890240255349198849 | https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg | 1 | 1 | Pembroke | 0.511319 | True |
| 5998 | 890240255349198849 | https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg | 1 | 2 | Cardigan | 0.451038 | True |
| 5999 | 890240255349198849 | https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg | 1 | 3 | Chihuahua | 0.029248 | True |
| 6000 | 890609185150312448 | https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg | 1 | 1 | Irish_terrier | 0.487574 | True |
| 6001 | 890609185150312448 | https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg | 1 | 2 | Irish_setter | 0.193054 | True |
| 6002 | 890609185150312448 | https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg | 1 | 3 | Chesapeake_Bay_retriever | 0.118184 | True |
| 6003 | 890729181411237888 | https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg | 2 | 1 | Pomeranian | 0.566142 | True |
| 6004 | 890729181411237888 | https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg | 2 | 2 | Eskimo_dog | 0.178406 | True |
| 6005 | 890729181411237888 | https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg | 2 | 3 | Pembroke | 0.076507 | True |
| 6006 | 890971913173991426 | https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg | 1 | 1 | Appenzeller | 0.341703 | True |
| 6007 | 890971913173991426 | https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg | 1 | 2 | Border_collie | 0.199287 | True |
| 6008 | 890971913173991426 | https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg | 1 | 3 | ice_lolly | 0.193548 | False |
| 6009 | 891087950875897856 | https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg | 1 | 1 | Chesapeake_Bay_retriever | 0.425595 | True |
| 6010 | 891087950875897856 | https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg | 1 | 2 | Irish_terrier | 0.116317 | True |
| 6011 | 891087950875897856 | https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg | 1 | 3 | Indian_elephant | 0.076902 | False |
| 6012 | 891327558926688256 | https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg | 2 | 1 | basset | 0.555712 | True |
| 6013 | 891327558926688256 | https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg | 2 | 2 | English_springer | 0.225770 | True |
| 6014 | 891327558926688256 | https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg | 2 | 3 | German_short-haired_pointer | 0.175219 | True |
| 6015 | 891689557279858688 | https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg | 1 | 1 | paper_towel | 0.170278 | False |
| 6016 | 891689557279858688 | https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg | 1 | 2 | Labrador_retriever | 0.168086 | True |
| 6017 | 891689557279858688 | https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg | 1 | 3 | spatula | 0.040836 | False |
| 6018 | 891815181378084864 | https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg | 1 | 1 | Chihuahua | 0.716012 | True |
| 6019 | 891815181378084864 | https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg | 1 | 2 | malamute | 0.078253 | True |
| 6020 | 891815181378084864 | https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg | 1 | 3 | kelpie | 0.031379 | True |
| 6021 | 892177421306343426 | https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg | 1 | 1 | Chihuahua | 0.323581 | True |
| 6022 | 892177421306343426 | https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg | 1 | 2 | Pekinese | 0.090647 | True |
| 6023 | 892177421306343426 | https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg | 1 | 3 | papillon | 0.068957 | True |
| 6024 | 892420643555336193 | https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg | 1 | 1 | orange | 0.097049 | False |
| 6025 | 892420643555336193 | https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg | 1 | 2 | bagel | 0.085851 | False |
| 6026 | 892420643555336193 | https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg | 1 | 3 | banana | 0.076110 | False |
6027 rows × 7 columns
Define
Merge twitter_clean with twitter_counts_clean using only keys from twitter_clean with 'tweet_id' as the column level names to join on
Code
#convert both Key columns str
twitter_counts_clean['tweet_id'] = twitter_counts_clean['tweet_id'].astype(str)
twitter_clean['tweet_id'] = twitter_clean['tweet_id'].astype(str)
# Merge twitter_clean with twitter_counts_clean.
twitter_complete = pd.merge(twitter_clean,twitter_counts_clean,how = 'left',on = 'tweet_id')
Test
twitter_complete.head()
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | corrected_names | dog_stage | retweets | favorites | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | 2017-08-01 16:23:56 | This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU | https://twitter.com/dog_rates/status/892420643555336193/photo/1 | 13.0 | 10.0 | Phineas | NaN | 8853 | 39467 |
| 1 | 892177421306343426 | 2017-08-01 00:17:27 | This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV | https://twitter.com/dog_rates/status/892177421306343426/photo/1 | 13.0 | 10.0 | Tilly | NaN | 6514 | 33819 |
| 2 | 891815181378084864 | 2017-07-31 00:18:03 | This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB | https://twitter.com/dog_rates/status/891815181378084864/photo/1 | 12.0 | 10.0 | Archie | NaN | 4328 | 25461 |
| 3 | 891689557279858688 | 2017-07-30 15:58:51 | This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ | https://twitter.com/dog_rates/status/891689557279858688/photo/1 | 13.0 | 10.0 | Darla | NaN | 8964 | 42908 |
| 4 | 891327558926688256 | 2017-07-29 16:00:24 | This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f | https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1 | 12.0 | 10.0 | Franklin | NaN | 9774 | 41048 |
#Store the clean DataFrames in a CSV file
twitter_complete.to_csv('twitter_complete.csv', index=False, encoding = 'utf-8')
img_pred_clean.to_csv('img_pred_clean.csv', index=False, encoding = 'utf-8')
# Read and Visualize twetter DataFrame
twitter_complete = pd.read_csv('twitter_complete.csv')
twitter_complete.head(3)
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | corrected_names | dog_stage | retweets | favorites | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | 2017-08-01 16:23:56 | This is Phineas. He's a mystical boy. Only eve... | https://twitter.com/dog_rates/status/892420643... | 13.0 | 10.0 | Phineas | NaN | 8853 | 39467 |
| 1 | 892177421306343426 | 2017-08-01 00:17:27 | This is Tilly. She's just checking pup on you.... | https://twitter.com/dog_rates/status/892177421... | 13.0 | 10.0 | Tilly | NaN | 6514 | 33819 |
| 2 | 891815181378084864 | 2017-07-31 00:18:03 | This is Archie. He is a rare Norwegian Pouncin... | https://twitter.com/dog_rates/status/891815181... | 12.0 | 10.0 | Archie | NaN | 4328 | 25461 |
# Read and Visualize image prediction DataFrame
img_pred_clean = pd.read_csv('img_pred_clean.csv')
img_pred_clean.head()
| tweet_id | jpg_url | img_num | prediction_number | pred | conf | dog | |
|---|---|---|---|---|---|---|---|
| 0 | 666020888022790149 | https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg | 1 | 1 | Welsh_springer_spaniel | 0.465074 | True |
| 1 | 666020888022790149 | https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg | 1 | 2 | collie | 0.156665 | True |
| 2 | 666020888022790149 | https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg | 1 | 3 | Shetland_sheepdog | 0.061428 | True |
| 3 | 666029285002620928 | https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg | 1 | 1 | redbone | 0.506826 | True |
| 4 | 666029285002620928 | https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg | 1 | 2 | miniature_pinscher | 0.074192 | True |
# Create a new rating column
twitter_complete['Rating']=twitter_complete.rating_numerator/twitter_complete.rating_denominator
twitter_complete.Rating.sort_values().value_counts()
1.200 481 1.000 437 1.100 414 1.300 287 0.900 153 0.800 98 0.700 51 1.400 39 0.500 33 0.600 32 0.300 19 0.400 15 0.200 9 0.100 4 1.127 1 1.350 1 0.975 1 1.126 1 0.000 1 Name: Rating, dtype: int64
# Change variables type
twitter_complete.tweet_id = twitter_complete.tweet_id.astype(str)
img_pred_clean.tweet_id = img_pred_clean.tweet_id.astype(str)
twitter_complete.dog_stage = twitter_complete.dog_stage.astype("category")
# Plot a heatmap for the twitter_complete dataframe
sns.heatmap(twitter_complete.corr(), fmt="d")
plt.title('Correlation-matrix', size=20)
plt.savefig('heatmap');
# Visualizing the linear relationship between retweets and favorites
sns.jointplot(x="retweets", y="favorites", data=twitter_complete,kind="reg", color="purple")
plt.title('Favorite vs. Retweet Count')
plt.xlabel('Number of Retweets')
plt.ylabel('Number of Favorites');
As expected, there is no strong correlation between two different variables except for retweets and favorites in the twitter_complete dataframe.
# Plot a heatmap for the img_pred_clean dataframe
sns.heatmap(img_pred_clean.corr(), fmt="d")
plt.title('Correlation-matrix', size=20)
plt.savefig('heatmap');
# Visualizing the linear relationship between retweets and favorites
sns.jointplot(x="prediction_number", y="conf", data=img_pred_clean,kind="reg", color="purple")
plt.title('Favorite vs. Retweet Count')
plt.xlabel('Number of Retweets')
plt.ylabel('Number of Favorites');
As expected, there is no strong correlation between two different variables except for Prediction_number and Confidence, as the negative correlation was already a norm in the dataset construction.
# Convert 'timestamp' column to datetime type
twitter_complete['timestamp'] = pd.to_datetime(twitter_complete['timestamp'])
# Create a new dataframe with the column 'timestamp' as the index
df_time=twitter_complete.set_index('timestamp')
# Show columns of the new dataframe
df_time.columns
Index(['tweet_id', 'text', 'expanded_urls', 'rating_numerator',
'rating_denominator', 'corrected_names', 'dog_stage', 'retweets',
'favorites', 'Rating'],
dtype='object')
# Plot the distribution of Retweets and Favorites over time
plt.style.use("seaborn")
df_time[['favorites','retweets']].plot(style='*', color=['purple','pink'], figsize=(12.2,5),fontsize=13)
plt.title('Favorites and Retweets with Time \n', fontsize=20)
plt.xlabel('Date', fontsize=17)
plt.ylabel('Count', fontsize=17);
As can be seen in the plot, the number of favorites increases over time, with a moderate number of favorites in November 2015 and growing progressively over the years until September 2017.
However, there is no progressive growth in the number of retweets, which seems to decrease its density in the second semester of the year 2017.
# Plot the distribution of Rating values over time
plt.style.use("seaborn")
df_time['Rating'].plot(style='.', color='c', figsize=(12.2,5), fontsize=13)
plt.title('Rating values over Time \n', fontsize=20)
plt.xlabel('Date' ,fontsize=17)
plt.ylabel('Count' ,fontsize=17);
In the figure above it is clearly seen how there are not ratings with values less than or equal to 0.8 (except for very specific outliers) after September 2016.
The high ratings have maintained its great popularity over timeÂ
# Change timestamp type to datetime
twitter_complete.timestamp = pd.to_datetime(twitter_complete['timestamp'], format='%Y-%m-%d %H:%M:%S.%f')
# Select the data containing the information for the number of tweets over time (year, month)
timed_data = twitter_complete.groupby(pd.Grouper(key = 'timestamp', freq = "M")).count().reset_index()
timed_data = timed_data[['timestamp', 'tweet_id']]
# Set the seaborn plotting style for the plot below
plt.style.use("seaborn")
# Plot Tweets Over Time
plt.plot(timed_data.timestamp, timed_data.tweet_id, color='purple',
marker = 'o', linestyle=':', linewidth=2)
plt.title('Tweets Over Time \n', fontsize=20)
plt.xlabel("Time (Year, Month)", labelpad=10, fontsize=15)
plt.ylabel("Number of tweets", labelpad=20, fontsize=15)
Text(0,0.5,'Number of tweets')
The number of tweets decreases over time, showing a sharp drop in December 2016.
# Choose a seaborn style
plt.style.use("seaborn-pastel")
# Plot the Most Rated Breeds with at least more than 80 records per breed
data=img_pred_clean.groupby('pred').filter(lambda x: len(x) >= 80)
data['pred'].value_counts().sort_values().plot(kind = 'barh', fontsize=14, figsize=(12.2,6), hatch='////')
plt.title('Most Popular Breeds \n', fontsize=20)
plt.xlabel("Number of tweets", labelpad=10, fontsize=15)
plt.ylabel("Dog Breed", labelpad=20, fontsize=15);
The most popular dog breeds are Golden Retriever and Labrador Retriever, followed by Chihuahua and Pembroke.
plt.style.use("seaborn-pastel")
df_names=twitter_complete.corrected_names.value_counts()
df_names[df_names > 7].sort_values().plot(kind='barh', legend=False, hatch='////')
plt.title('Most Popular Names \n', fontsize=20)
plt.xlabel("Number of tweets", labelpad=10, fontsize=15)
plt.ylabel("Dog Name", labelpad=20, fontsize=15);
The most popular dog names are Charlie and Lucy, followed by Cooper and Oliver.
plt.style.use("seaborn-pastel")
df_stages=twitter_complete.dog_stage.value_counts()
df_stages.sort_values().plot(kind='barh', legend=False, hatch='////')
plt.title('Most Popular Stages \n', fontsize=20)
plt.xlabel("Number of tweets", labelpad=10, fontsize=15)
plt.ylabel("Dog Stage", labelpad=20, fontsize=15)
Text(0,0.5,'Dog Stage')
The most popular dog stage is by far pupper, followed by Doggo, Puppo and Floofer.
plt.style.use("seaborn-pastel")
sns.boxplot(x='dog_stage', y='Rating', data=twitter_complete)
plt.title('Dog Stages Rating \n', fontsize=20)
plt.xlabel('Dog Stage', labelpad=20, fontsize=15)
plt.ylabel('Rating', labelpad=20, fontsize=15)
plt.ylim(0.7,);
"Puppo" dogs got the highest rating and are the less rated, while "Puppers" got the lowest rating and are the most rated ones.
plt.scatter(twitter_complete.Rating, twitter_complete.favorites, alpha=0.8, c='purple', s=30, label="favorites", marker = '*')
plt.scatter(twitter_complete.Rating, twitter_complete.retweets, alpha=0.8, c='pink', s=30, label="retweets", marker = '*')
plt.legend(loc=2)
plt.title('Favorites and Retweets vs. Rating \n', fontsize=20)
plt.xlabel('Rating', labelpad=20, fontsize=15)
plt.ylabel('Number of tweets', labelpad=20, fontsize=15)
Text(0,0.5,'Number of tweets')
The number of retweets and favorites increases with the Rating value, reaching the bullish peak for a rating of 13.
# Create a Dataframe with only true first predictions
img_pred_1 = img_pred_clean[(img_pred_clean['prediction_number']==1)&(img_pred_clean['dog'])]
# Merge twitter_complete with img_pred_1
twitter_complete_pred1 = pd.merge(twitter_complete,img_pred_1, how = 'left',on = 'tweet_id')
twitter_complete_pred1 = twitter_complete_pred1.replace({np.nan: 'No Picture' })
twitter_complete_pred1 = twitter_complete_pred1.replace({True: 'Picture'}).drop(['prediction_number','img_num'],axis=1)
twitter_complete_pred1.head(3)
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | corrected_names | dog_stage | retweets | favorites | Rating | jpg_url | pred | conf | dog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | 2017-08-01 16:23:56 | This is Phineas. He's a mystical boy. Only eve... | https://twitter.com/dog_rates/status/892420643... | 13 | 10.0 | Phineas | No Picture | 8853 | 39467 | 1.3 | No Picture | No Picture | No Picture | No Picture |
| 1 | 892177421306343426 | 2017-08-01 00:17:27 | This is Tilly. She's just checking pup on you.... | https://twitter.com/dog_rates/status/892177421... | 13 | 10.0 | Tilly | No Picture | 6514 | 33819 | 1.3 | https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg | Chihuahua | 0.323581 | Picture |
| 2 | 891815181378084864 | 2017-07-31 00:18:03 | This is Archie. He is a rare Norwegian Pouncin... | https://twitter.com/dog_rates/status/891815181... | 12 | 10.0 | Archie | No Picture | 4328 | 25461 | 1.2 | https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg | Chihuahua | 0.716012 | Picture |
avg_conf=twitter_complete_pred1[twitter_complete_pred1['conf']!="No Picture"].conf.mean()
print('The average confidence level for the true picture`s prediction is:',avg_conf*100, '%')
The average confidence level for the true picture`s prediction is: 61.3978574033 %
ret_nopic=twitter_complete_pred1[twitter_complete_pred1['dog']=="No Picture"].retweets.sum()
ret_pic=twitter_complete_pred1[twitter_complete_pred1['dog']=="Picture"].retweets.sum()
print('The number of retweets of tweets WITH a picture is:',ret_pic)
print('The number of retweets of tweets WITHOUT a picture is:',ret_nopic)
print('The percentage difference is:',((ret_pic/(ret_nopic+ret_pic))-(ret_nopic/(ret_nopic+ret_pic)))*100,'%')
The number of retweets of tweets WITH a picture is: 4057446 The number of retweets of tweets WITHOUT a picture is: 1845634 The percentage difference is: 37.4687790103 %
fav_nopic=twitter_complete_pred1[twitter_complete_pred1['dog']=="No Picture"].favorites.sum()
fav_pic=twitter_complete_pred1[twitter_complete_pred1['dog']=="Picture"].favorites.sum()
print('The number of favorites of tweets WITH a picture is:',fav_pic)
print('The number of favorites of tweets WITHOUT a picture is:',fav_nopic)
print('The percentage difference is:',((fav_pic/(fav_nopic+fav_pic))-(fav_nopic/(fav_nopic+fav_pic)))*100,'%')
The number of favorites of tweets WITH a picture is: 13530488 The number of favorites of tweets WITHOUT a picture is: 5150552 The percentage difference is: 44.857973646 %
Therefore, tweets have more retweets and favorites if they include an image, also, this fact affects the favorites more.
golden_retriever_df = twitter_complete.merge(img_pred_clean.loc[img_pred_clean['pred'] == 'golden_retriever',
['tweet_id','jpg_url','pred']],how = 'right',on = 'tweet_id')
top_golden_retriever=golden_retriever_df[(golden_retriever_df['Rating']==1.20)&(golden_retriever_df['corrected_names']!=np.nan)]
top_favs_golden_retriever=top_golden_retriever.sort_values(by = 'favorites', ascending=False)
top_favs_golden_retriever.head()
| tweet_id | timestamp | text | expanded_urls | rating_numerator | rating_denominator | corrected_names | dog_stage | retweets | favorites | Rating | jpg_url | pred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 30 | 837820167694528512 | 2017-03-04 00:21:08 | Here's a pupper before and after being asked "... | https://twitter.com/dog_rates/status/837820167... | 12.0 | 10.0 | NaN | pupper | 8952.0 | 37277.0 | 1.2 | https://pbs.twimg.com/media/C6CI_jbVAAA3-a1.jpg | golden_retriever |
| 56 | 814153002265309185 | 2016-12-28 16:56:16 | This is Oshie. He's ready to party. Bought tha... | https://twitter.com/dog_rates/status/814153002... | 12.0 | 10.0 | Oshie | NaN | 10080.0 | 32000.0 | 1.2 | https://pbs.twimg.com/media/C0xz04SVIAAeyDb.jpg | golden_retriever |
| 87 | 788765914992902144 | 2016-10-19 15:37:03 | This is Butter. She can have whatever she want... | https://twitter.com/dog_rates/status/788765914... | 12.0 | 10.0 | Butter | NaN | 12014.0 | 30658.0 | 1.2 | https://pbs.twimg.com/media/CvJCabcWgAIoUxW.jpg | golden_retriever |
| 90 | 786963064373534720 | 2016-10-14 16:13:10 | This is Rory. He's got an interview in a few m... | https://twitter.com/dog_rates/status/786963064... | 12.0 | 10.0 | Rory | NaN | 9327.0 | 29725.0 | 1.2 | https://pbs.twimg.com/media/Cuvau3MW8AAxaRv.jpg | golden_retriever |
| 1 | 888917238123831296 | 2017-07-23 00:22:39 | This is Jim. He found a fren. Taught him how t... | https://twitter.com/dog_rates/status/888917238... | 12.0 | 10.0 | Jim | NaN | 4709.0 | 29611.0 | 1.2 | https://pbs.twimg.com/media/DFYRgsOUQAARGhO.jpg | golden_retriever |
# Import necessary libraries
from PIL import Image
import requests
from io import BytesIO
# Display Images with request.get(url)
url_1=top_favs_golden_retriever.loc[30,'jpg_url']
response = requests.get(url_1)
Image.open(BytesIO(response.content))
url_2=top_favs_golden_retriever.loc[56,'jpg_url']
response = requests.get(url_2)
Image.open(BytesIO(response.content))
url_3=top_favs_golden_retriever.loc[87,'jpg_url']
response = requests.get(url_3)
Image.open(BytesIO(response.content))
url_4=top_favs_golden_retriever.loc[90,'jpg_url']
response = requests.get(url_4)
Image.open(BytesIO(response.content))
url_5=top_favs_golden_retriever.loc[1,'jpg_url']
response = requests.get(url_5)
Image.open(BytesIO(response.content))